Not Applicable.
As background, in the remote sensing/aerial imaging industry, imagery is used to capture views of a geographic area and to be able to measure objects and structures within the images as well as to be able to determine geographic locations of points within the image. These are generally referred to as “geo-referenced images” and come in two basic categories:
Captured Imagery—these images have the appearance they were captured by the camera or sensor employed.
Projected Imagery—these images have been processed and converted such that they confirm to a mathematical projection.
All imagery starts as captured imagery, but as most software cannot geo-reference captured imagery, that imagery is then reprocessed to create the projected imagery. The most common form of projected imagery is the ortho-rectified image. This process aligns the image to an orthogonal or rectilinear grid (composed of rectangles). The input image used to create an ortho-rectified image is a nadir image—that is, an image captured with the camera pointing straight down. It is often quite desirable to combine multiple images into a larger composite image such that the image covers a larger geographic area on the ground. The most common form of this composite image is the “ortho-mosaic image” which is an image created from a series of overlapping or adjacent nadir images that are mathematically combined into a single ortho-rectified image.
When creating an ortho-mosaic, this same ortho-rectification process is used, however, instead of using only a single input nadir image, a collection of overlapping or adjacent nadir images are used and they are combined to form a single composite ortho-rectified image known as an ortho-mosaic. In general, the ortho-mosaic process entails the following steps:
A rectilinear grid is created, which results in an ortho-mosaic image where every grid pixel covers the same amount of area on the ground.
The location of each grid pixel is determined from the mathematical definition of the grid. Generally, this means the grid is given an X and Y starting or origin location and an X and Y size for the grid pixels. Thus, the location of any pixel is simply the origin location plus the number of pixels times the size of each pixel. In mathematical terms: Xpixel=Xorigin+Xsize×Columnpixel and Ypixel=Yorigin+Ysize×Rowpixel.
The available nadir images are checked to see if they cover the same point on the ground as the grid pixel being filled. If so, a mathematical formula is used to determine where that point on the ground projects up onto the camera's pixel image map and that resulting pixel value is then transferred to the grid pixel.
Because the rectilinear grids used for the ortho-mosaic are generally the same grids used for creating maps, the ortho-mosaic images bear a striking similarity to maps and as such, are generally very easy to use from a direction and orientation standpoint.
In producing the geo-referenced aerial images, hardware and software systems designed for georeferencing airborne sensor data exist. For example, a method and apparatus for mapping and measuring land is described in U.S. Pat. No. 5,247,356. In addition, a system produced by Applanix Corporation of Richmond Hill, Ontario, Canada and sold under the trademark “POS AV” provides a hardware and software system for directly georeferencing sensor data. Direct Georeferencing is the direct measurement of sensor position and orientation (also known as the exterior orientation parameters), without the need for additional ground information over the project area. These parameters allow data from the airborne sensor to be georeferenced to the Earth or local mapping frame. Examples of airborne sensors include: aerial cameras (digital or film-based), multi-spectral or hyper-spectral scanners, SAR, or LIDAR.
The POS AV system was mounted on a moving platform, such as an airplane, such that the airborne sensor was pointed toward the Earth. The positioning system received position signals from a satellite constellation and also received time signals from an accurate clock. The sensor was controlled by a computer running flight management software to take images. Signals indicative of the taking of an image were sent from the sensor to the positioning system to record the time and position where the image was taken.
When capturing images with a digital sensor, a variety of abnormalities such as elevated sensor noise levels, streaks, blooms or smears can be formed within the captured image. Such abnormalities can be caused by malfunctions of the image capture device, or by the external environment. For example, in aerial photography, in particular, reflections of the sun off of shiny or reflective surfaces such as lakes, windows, greenhouses or windshields can cause blooms which smear to form streaks in the captured image. An exemplary photograph having a streak formed from reflections off of water is shown in
Therefore, there is a need to eliminate the time delays and costly re-flights associated with abnormalities occurring in captured aerial imagery. It is to such a system for eliminating the time delays and costly re-flights that the present invention is directed.
Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction, experiments, exemplary data, and/or the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments or being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for purposes of description and should not be regarded as limiting.
Referring to the drawings, and in particular to
The images can be oblique images, orthogonal images, or nadir images, or combinations thereof.
As shown in
In certain embodiments depicted in
The image capture devices 14 are mounted to the moving platform 21, and once mounted are typically calibrated so that the exact position and orientation of the image capture devices 14 are known with respect to at least a portion of the moving platform 21. For example, as shown in
Each of the image capture devices 14 has a sensor (e.g.,
The monitoring system 16 records data indicative of the capturing of the images. For example, the monitoring system 16 can record position data as a function of time, time data and/or orientation data. In the embodiments depicted in
In the embodiments depicted in
The computer system 20 receives and stores (preferably in the database 38) the information indicative of the order of events indicated by the event signals, and identification of image capture devices 14 providing the event signals. The computer system 20 optionally also receives and stores the images (preferably in the database 38) generated by the image capture devices 14. The monitoring system 16 records the data indicative of the capturing of images by storing it internally, outputting it to the computer system 20, or outputting such data in any other suitable manner, such as storing such data on an external magnetic or optical storage system. The position related to the moving platform 21 can be provided in any suitable coordinate system, such as an X, Y, Z coordinate system.
Further, the image capture system 10 can be provided with an orientation system, such as an inertial measurement unit 40 for capturing other types of information with respect to the moving platform 21, such as the orientation of the moving platform 21. The inertial measurement unit 40 can be provided with a variety of sensors, such as accelerometers (not shown) for determining the roll, pitch and yaw related to the moving platform 21. Further, it should be understood that the position and/or orientation information does not necessarily have to be a position and/or orientation of the moving platform 21. The position and orientation information is simply related to the moving platform 21, i.e. the position and/or orientation of the moving platform 21 should be able to be determined by the information recorded by the monitoring system 16. For example, the position and orientation information can be provided for a device connected to the moving platform 21. Then, the position and orientation for each image capture device can be determined based upon their known locations relative to the moving platform 21.
In using the systems depicted in
In using the system depicted in
Referring now to
The image capture system 10b is similar in construction and function to the image capture system 10 or 10a described above, with the exception that the image capture system 10b (shown in
In the example shown in
To aid the detection of abnormalities, the image capture system 10b preferably utilizes digital cameras with each digital camera having one or more sensor 114. A diagrammatic view of the sensor 114 is shown in
The sensor 114 has an image area 130 and a dark area 132 bordering the image area 130. The dark area 132 can serve as a reference to the image area 130. The dark area 132 may be referred to herein as a “reference area”. The image area 130 is shown in light gray, and the dark area 132 is shown in darker gray. The photosites 124a and 124b are located in the image area 130 while the photosite 124c is located in the dark area 132. The sensor 114 can be configured as an area array sensor with photosites arranged in a grid pattern covering the entire image area 130 and at least part of the dark area 132. When the image is read from the sensor 114, the stored electrons are converted to a series of analog charges which are then converted to digital values by an Analog-to-Digital (A to D) converter (not shown).
Once the sensor 114 has captured the image, it must be read, converted to digital, and then stored. The image can be stored and logged in the manner described above. The charges stored on the sensor 114 are typically not read all at once but a row, pixel or column at a time. When a row or column is read at a time, pixel values in each row or column are read in a sequential manner by moving the pixel values up or down the row or column through the dark area 132 of the sensor 114 as indicated by an arrow 134.
To detect an abnormality, the abnormality detection algorithm 103-2 scans the image utilizing predetermined parameters indicative of characteristics of abnormalities. One method to locate certain types of abnormalities, is to monitor the pixel values (or an average of the pixel values) in the dark area 132 as the pixel values are being moved through the dark area 132. Another method is to scan/analyze the image using pattern recognition techniques to locate one or more abnormality. For example, the image can be scanned/analyzed after it has been moved through the dark area 132 and stored in memory.
As an example, shown in
When the pixel values exceed a predetermined or dynamic threshold value indicative of a streak or hot spot, then the abnormality detection algorithm 103-2 causes the detection computer 103-1 to output a signal causing one or more immediate re-shoot(s) of the image. The term “immediate” as used herein means occurring, acting, or accomplished without substantial loss or interval of time. The interval of time between the capturing of the first and second images 104 and 106 may depend upon a variety of factors, such as the time involved in detecting the abnormality, the size or type of the abnormality, and the time involved in actuating the image capture device 14 or 14a to capture the second image 106.
To capture the portion of the object originally scheduled to be captured, the abnormality detection algorithm 103-2 can cause one or more re-shoots without detecting whether the abnormality is captured in the re-shot images, or the abnormality detection algorithm 103-2 can scan each re-shot image and cause another re-shoot until the earlier of (1) a re-shot image not containing an abnormality, or (2) the next scheduled image to be taken by the image capture device 14 or 14a.
Alternatively, the abnormality detection algorithm 103-2 can flag an image as “bad” and cause the detection computer 103-1 to send a signal to the flight management software executed on the computer systems 20 or 20a to automatically schedule a re-shoot for a future time. Preferably, the detection computer 103-1 schedules a re-shoot of the image such that the image is retaken before landing of the airplane.
It should be understood that certain of the processes described above, such as the formation of the third image 108, can be performed with the aid of a computer system running image processing software adapted to perform the functions described above. Further, the first, second and third images and data, as well as the abnormality detection algorithm 103-2 are stored on one or more computer readable mediums. Examples of a computer readable medium include an optical storage device, a magnetic storage device, an electronic storage device or the like. The term “Computer System” as used herein means a system or systems that are able to embody and/or execute the logic of the processes, such as the abnormality detection algorithm 103-2, described herein. The logic embodied in the form of software instructions or firmware may be executed on any appropriate hardware which may be a dedicated system or systems, or a general purpose computer system, or distributed processing computer system, all of which are well understood in the art, and a detailed description of how to make or use such computers is not deemed necessary herein. The detection computer 103-1 can be the same physical computer as the computer systems 20 or 20a, or different from the computer systems 20 or 20a. In one embodiment, the image capture system 10b includes a detection computer implemented as a part of one of the image capture devices 14 or 14a. For example, the image capture system 10b can include multiple detection computers with each detection computer implemented as a part of one image capture device 14 or 14a. In this embodiment, each of the one or more detection computers monitors the images being captured by its respective image capture device 14 or 14a and can cause a re-shoot by either passing a signal to the computer systems 20 or 20a, or by passing a signal directly to the image capture device 14 or 14a.
It will be understood from the foregoing description that various modifications and changes may be made in the preferred and alternative embodiments of the present invention without departing from its true spirit.
This description is intended for purposes of illustration only and should not be construed in a limiting sense. The scope of this invention should be determined only by the language of the claims that follow. The term “comprising” within the claims is intended to mean “including at least” such that the recited listing of elements in a claim are an open group. “A,” “an” and other singular terms are intended to include the plural forms thereof unless specifically excluded.
The present patent application claims priority to the patent application identified by U.S. Ser. No. 16/266,852, filed Feb. 4, 2019, which is a continuation of U.S. Ser. No. 15/965,086, filed Apr. 27, 2018, now U.S. Pat. No. 10,198,803, which is a continuation of U.S. Ser. No. 15/493,434, filed Apr. 21, 2017, now U.S. Pat. No. 9,959,609, which is a continuation of U.S. Ser. No. 15/043,068, filed on Feb. 12, 2016, now U.S. Pat. No. 9,633,425; which is a continuation of U.S. Ser. No. 13/744,174, filed Jan. 17, 2013, now U.S. Pat. No. 9,262,818; which claims priority to the patent application identified by U.S. Ser. No. 12/112,837, filed Apr. 30, 2008, now U.S. Pat. No. 8,385,672, which claims priority to the provisional patent application identified by U.S. Ser. No. 60/926,985 filed May 1, 2007, the entire content of all of which are hereby incorporated herein by reference.
Number | Name | Date | Kind |
---|---|---|---|
2273876 | Lutz et al. | Feb 1942 | A |
2944151 | Whitney et al. | Jul 1960 | A |
2979832 | Klemperer | Apr 1961 | A |
3153784 | Petrides et al. | Oct 1964 | A |
3594556 | Edwards | Jul 1971 | A |
3614410 | Bailey | Oct 1971 | A |
3621326 | Hobrough | Nov 1971 | A |
3661061 | Tokarz | May 1972 | A |
3699245 | Scott | Oct 1972 | A |
3716669 | Watanabe et al. | Feb 1973 | A |
3725563 | Woycechowsky | Apr 1973 | A |
3864513 | Halajian et al. | Feb 1975 | A |
3866602 | Furihata | Feb 1975 | A |
3877799 | O'Donnell | Apr 1975 | A |
3917199 | Dewitt | Nov 1975 | A |
4015080 | Moore-Searson | Mar 1977 | A |
4044879 | Stahl | Aug 1977 | A |
4184711 | Wakimoto | Jan 1980 | A |
4240108 | Levy | Dec 1980 | A |
4281354 | Conte | Jul 1981 | A |
4344146 | Davis et al. | Aug 1982 | A |
4344683 | Stemme | Aug 1982 | A |
4360876 | Girault et al. | Nov 1982 | A |
4382678 | Thompson et al. | May 1983 | A |
4387056 | Stowe | Jun 1983 | A |
4396942 | Gates | Aug 1983 | A |
4463380 | Hooks | Jul 1984 | A |
4489322 | Zulch et al. | Dec 1984 | A |
4489389 | Beckwith et al. | Dec 1984 | A |
4490742 | Wurtzinger | Dec 1984 | A |
4491399 | Bell | Jan 1985 | A |
4495500 | Vickers | Jan 1985 | A |
4527055 | Harkless et al. | Jul 1985 | A |
4543603 | Laures | Sep 1985 | A |
4586138 | Mullenhoff et al. | Apr 1986 | A |
4635136 | Ciampa et al. | Jan 1987 | A |
4653136 | Denison | Mar 1987 | A |
4653316 | Fukuhara | Mar 1987 | A |
4673988 | Jansson et al. | Jun 1987 | A |
4685143 | Choate | Aug 1987 | A |
4686474 | Olsen et al. | Aug 1987 | A |
4688092 | Kamel et al. | Aug 1987 | A |
4689748 | Hofmann | Aug 1987 | A |
4707698 | Constant et al. | Nov 1987 | A |
4758850 | Archdale et al. | Jul 1988 | A |
4805033 | Nishikawa | Feb 1989 | A |
4807024 | Mclaurin et al. | Feb 1989 | A |
4814711 | Olsen et al. | Mar 1989 | A |
4814896 | Heitzman et al. | Mar 1989 | A |
4843463 | Michetti | Jun 1989 | A |
4899296 | Khattak | Feb 1990 | A |
4906198 | Cosimano et al. | Mar 1990 | A |
4953227 | Katsuma et al. | Aug 1990 | A |
4956872 | Kimura | Sep 1990 | A |
5034812 | Rawlings | Jul 1991 | A |
5072396 | Fitzpatrick et al. | Dec 1991 | A |
5086314 | Aoki et al. | Feb 1992 | A |
5121222 | Endoh et al. | Jun 1992 | A |
5124913 | Sezan et al. | Jun 1992 | A |
5136297 | Lux et al. | Aug 1992 | A |
5138444 | Hiramatsu | Aug 1992 | A |
5155597 | Lareau et al. | Oct 1992 | A |
5164825 | Kobayashi et al. | Nov 1992 | A |
5166789 | Myrick | Nov 1992 | A |
5191174 | Chang et al. | Mar 1993 | A |
5200793 | Ulich et al. | Apr 1993 | A |
5210586 | Grage et al. | May 1993 | A |
5231435 | Blakely | Jul 1993 | A |
5247356 | Ciampa | Sep 1993 | A |
5251037 | Busenberg | Oct 1993 | A |
5265173 | Griffin et al. | Nov 1993 | A |
5267042 | Tsuchiya et al. | Nov 1993 | A |
5270756 | Busenberg | Dec 1993 | A |
5296884 | Honda et al. | Mar 1994 | A |
5335072 | Tanaka et al. | Aug 1994 | A |
5342999 | Frei et al. | Aug 1994 | A |
5345086 | Bertram | Sep 1994 | A |
5353055 | Hiramatsu | Oct 1994 | A |
5369443 | Woodham | Nov 1994 | A |
5381338 | Wysocki et al. | Jan 1995 | A |
5402170 | Parulski et al. | Mar 1995 | A |
5414462 | Veatch | May 1995 | A |
5426476 | Fussell et al. | Jun 1995 | A |
5467271 | Abel et al. | Nov 1995 | A |
5481479 | Wight et al. | Jan 1996 | A |
5486948 | Imai et al. | Jan 1996 | A |
5506644 | Suzuki et al. | Apr 1996 | A |
5508736 | Cooper | Apr 1996 | A |
5555018 | von Braun | Sep 1996 | A |
5586204 | Noble et al. | Dec 1996 | A |
5596494 | Kuo | Jan 1997 | A |
5604534 | Hedges et al. | Feb 1997 | A |
5613013 | Schuette | Mar 1997 | A |
5617224 | Ichikawa et al. | Apr 1997 | A |
5633946 | Lachinski et al. | May 1997 | A |
5654890 | Nicosia | Aug 1997 | A |
5668593 | Lareau et al. | Sep 1997 | A |
5677515 | Selk et al. | Oct 1997 | A |
5798786 | Lareau et al. | Aug 1998 | A |
5835133 | Moreton et al. | Nov 1998 | A |
5841574 | Willey | Nov 1998 | A |
5844602 | Lareau et al. | Dec 1998 | A |
5852753 | Lo et al. | Dec 1998 | A |
5894323 | Kain et al. | Apr 1999 | A |
5899945 | Baylocq et al. | May 1999 | A |
5963664 | Kumar et al. | Oct 1999 | A |
6037945 | Loveland | Mar 2000 | A |
6088055 | Lareau et al. | Jul 2000 | A |
6091448 | Washisu et al. | Jul 2000 | A |
6094215 | Sundahl et al. | Jul 2000 | A |
6097854 | Szeliski et al. | Aug 2000 | A |
6108032 | Hoagland | Aug 2000 | A |
6130705 | Lareau et al. | Oct 2000 | A |
6157747 | Szeliski et al. | Dec 2000 | A |
6167300 | Cherepenin et al. | Dec 2000 | A |
6222538 | Anderson | Apr 2001 | B1 |
6222583 | Matsumura et al. | Apr 2001 | B1 |
6236886 | Cherepenin et al. | May 2001 | B1 |
6256057 | Mathews et al. | Jul 2001 | B1 |
6373522 | Mathews et al. | Apr 2002 | B2 |
6421610 | Carroll et al. | Jul 2002 | B1 |
6434280 | Peleg et al. | Aug 2002 | B1 |
6597818 | Kumar et al. | Jul 2003 | B2 |
6639596 | Shum et al. | Oct 2003 | B1 |
6711475 | Murphy | Mar 2004 | B2 |
6731329 | Feist et al. | May 2004 | B1 |
6747686 | Bennett | Jun 2004 | B1 |
6809763 | Yoshida | Oct 2004 | B1 |
6810383 | Loveland | Oct 2004 | B1 |
6816819 | Loveland | Nov 2004 | B1 |
6826539 | Loveland | Nov 2004 | B2 |
6829584 | Loveland | Dec 2004 | B2 |
6834128 | Altunbasak et al. | Dec 2004 | B1 |
6876763 | Sorek et al. | Apr 2005 | B2 |
6961445 | Jensen et al. | Nov 2005 | B1 |
7006678 | Sawada | Feb 2006 | B2 |
7009638 | Gruber et al. | Mar 2006 | B2 |
7018050 | Ulichney et al. | Mar 2006 | B2 |
7046401 | Dufaux et al. | May 2006 | B2 |
7061650 | Walmsley et al. | Jun 2006 | B2 |
7065260 | Zhang et al. | Jun 2006 | B2 |
7113202 | Konya | Sep 2006 | B2 |
7123382 | Walmsley et al. | Oct 2006 | B2 |
7127107 | Kubota et al. | Oct 2006 | B2 |
7127348 | Smitherman et al. | Oct 2006 | B2 |
7133551 | Chen | Nov 2006 | B2 |
7142984 | Rahmes et al. | Nov 2006 | B2 |
7181074 | Okada et al. | Feb 2007 | B2 |
7184072 | Loewen et al. | Feb 2007 | B1 |
7230221 | Busse et al. | Jun 2007 | B2 |
7233691 | Setterholm | Jun 2007 | B2 |
7262790 | Bakewell | Aug 2007 | B2 |
7298876 | Marshall et al. | Nov 2007 | B1 |
7348895 | Lagassey | Mar 2008 | B2 |
7379091 | Yost et al. | May 2008 | B2 |
7383504 | Divakaran et al. | Jun 2008 | B1 |
7508423 | Ohmori et al. | Mar 2009 | B2 |
7509241 | Guo | Mar 2009 | B2 |
7590284 | Kakiuchi et al. | Sep 2009 | B2 |
7659906 | LinneVonBerg et al. | Feb 2010 | B2 |
7702461 | Conner et al. | Apr 2010 | B2 |
RE41447 | Tiana | Jun 2010 | E |
7728833 | Verma | Jun 2010 | B2 |
7826666 | Hamza et al. | Nov 2010 | B2 |
7832267 | Woro | Nov 2010 | B2 |
7844499 | Yahiro | Nov 2010 | B2 |
7876925 | Hamza | Jan 2011 | B2 |
8078396 | Meadow | Dec 2011 | B2 |
8705843 | Lieckfeldt | Apr 2014 | B2 |
20020041328 | LeCompte et al. | Apr 2002 | A1 |
20020041717 | Murata et al. | Apr 2002 | A1 |
20020114536 | Xiong et al. | Aug 2002 | A1 |
20030014224 | Guo et al. | Jan 2003 | A1 |
20030030734 | Gibbs et al. | Feb 2003 | A1 |
20030043824 | Remboski et al. | Mar 2003 | A1 |
20030088362 | Melero et al. | May 2003 | A1 |
20030151674 | Lin | Aug 2003 | A1 |
20030164962 | Nims et al. | Sep 2003 | A1 |
20030179942 | Okada | Sep 2003 | A1 |
20030193602 | Satoh et al. | Oct 2003 | A1 |
20030214585 | Bakewell | Nov 2003 | A1 |
20040105090 | Schultz et al. | Jun 2004 | A1 |
20040167709 | Smitherman et al. | Aug 2004 | A1 |
20040189837 | Kido | Sep 2004 | A1 |
20040196503 | Kurtenbach et al. | Oct 2004 | A1 |
20040263628 | Ambiru et al. | Dec 2004 | A1 |
20050024517 | Luciano | Feb 2005 | A1 |
20050073241 | Yamauchi et al. | Apr 2005 | A1 |
20050088251 | Matsurnoto | Apr 2005 | A1 |
20050094004 | Gotanda | May 2005 | A1 |
20050117031 | Russon et al. | Jun 2005 | A1 |
20050169521 | Hel-Or | Aug 2005 | A1 |
20060028550 | Palmer et al. | Feb 2006 | A1 |
20060092043 | Lagassey | May 2006 | A1 |
20060132482 | Oh | Jun 2006 | A1 |
20060152303 | Liang et al. | Jul 2006 | A1 |
20060152606 | Noguchi | Jul 2006 | A1 |
20060171567 | Osher et al. | Aug 2006 | A1 |
20060238383 | Kimchi et al. | Oct 2006 | A1 |
20060250515 | Koseki et al. | Nov 2006 | A1 |
20070024612 | Balfour | Feb 2007 | A1 |
20070046448 | Smitherman | Mar 2007 | A1 |
20070237420 | Steedly et al. | Oct 2007 | A1 |
20080120031 | Rosenfeld et al. | May 2008 | A1 |
20080123994 | Schultz et al. | May 2008 | A1 |
20080158256 | Russell et al. | Jul 2008 | A1 |
20080231700 | Schultz et al. | Sep 2008 | A1 |
20090067725 | Sasakawa et al. | Mar 2009 | A1 |
20090177458 | Hochart et al. | Jul 2009 | A1 |
20090190847 | Marks | Jul 2009 | A1 |
20090208095 | Zebedin | Aug 2009 | A1 |
20090304227 | Kennedy et al. | Dec 2009 | A1 |
20100296693 | Thornberry et al. | Nov 2010 | A1 |
20110033110 | Shimamura et al. | Feb 2011 | A1 |
20130246204 | Thornberry et al. | Sep 2013 | A1 |
Number | Date | Country |
---|---|---|
331204 | Jul 2006 | AT |
0316110 | Sep 2005 | BR |
2402234 | Sep 2000 | CA |
2505566 | May 2004 | CA |
1735897 | Feb 2006 | CN |
60017384 | Mar 2006 | DE |
60306301 | Nov 2006 | DE |
1418402 | Oct 2006 | DK |
1010966 | Feb 1999 | EP |
1180967 | Feb 2002 | EP |
1418402 | May 2004 | EP |
1696204 | Aug 2006 | EP |
2266704 | Mar 2007 | ES |
2003317089 | Nov 2003 | JP |
PA05004987 | Feb 2006 | MX |
WO9918732 | Apr 1999 | WO |
WO2000053090 | Sep 2000 | WO |
WO2004044692 | May 2004 | WO |
WO2005088251 | Sep 2005 | WO |
WO2008028040 | Mar 2008 | WO |
Entry |
---|
Ackermann, Prospects of Kinematic GPS Aerial Triangulation, ITC Journal, 1992. |
Ciampa, John A., “Pictometry Digital Video Mapping”, SPIE, vol. 2598, pp. 140-148, 1995. |
Ciampa, J. A., Oversee, Presented at Reconstruction After Urban earthquakes, Buffalo, NY, 1989. |
Dunford et al., Remote Sensing for Rural Development Planning in Africa, The Journal for the International Institute for Aerial Survey and Earth Sciences, 2:99-108, 1983. |
Gagnon, P.A., Agnard, J. P., Nolette, C., & Boulianne, M., “A Micro-Computer based General Photogrammetric System”, Photogrammetric Engineering and Remote Sensing, vol. 56, No. 5., pp. 623-625, 1990. |
Konecny, G., “Issues of Digital Mapping”, Leibniz University Hannover, Germany, GIS Ostrava 2008, Ostrava Jan. 27-30, 2008, pp. 1-8. |
Konecny, G., “Analytical Aerial Triangulation with Convergent Photography”, Department of Surveying Engineering, University of New Brunswick, pp, 37-57, 1966. |
Konecny, G., “Interior Orientation and Convergent Photography”, Photogrammetric Engineering, pp. 625-634, 1965. |
Graham, Lee A., “Airborne Video for Near-Real-Time Vegetation Mapping”, Journal of Forestry, 8:28-32, 1993. |
Graham, Horita TRG-50 SMPTE Time-Code Reader, Generator, Window Inserter, 1990. |
Hess, L.L, et al., “Geocoded Digital Videography for Validation of Land Cover Mapping in the Amazon Basin”, International Journal of Remote Sensing, vol. 23, No. 7, pp. 1527-1555, 2002. |
Hinthorne, J., et al., “Image Processing in the Grass GIS”, Geoscience and Remote Sensing Symposium, 4:2227-2229, 1991. |
Imhof, Ralph K., “Mapping from Oblique Photographs”, Manual of Photogrammetry, Chapter 18, 1966. |
Jensen, John R., Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice-Hall, 1986; 399 pages. |
Lapine, Lewis A., “Practical Photogrammetric Control by Kinematic GPS”, GPS World, 1(3):44-49, 1990. |
Lapine, Lewis A., Airborne Kinematic GPS Positioning for Photograrnmetry—The Determination of the Camera Exposure Station, Silver Spring, MD, 11 pages, at least as early as 2000. |
Linden et al., Airborne Video Automated Processing, US Forest Service Internal report, Fort Collins, CO, 1993. |
Myhre, Dick, “Airborne Video System Users Guide”, USDA Forest Service, Forest Pest Management Applications Group, published by Management Assistance Corporation of America, 6 pages, 1992. |
Myhre et al., “An Airborne Video System Developed Within Forest Pest Management—Status and Activities”, 10 pages, 1992. |
Myhre et al., “Airborne Videography—A Potential Tool for Resource Managers”—Proceedings: Resource Technology 90, 2nd International Symposium on Advanced Technology in Natural Resource Management, 5 pages, 1990. |
Myhre et al., Aerial Photography for Forest Pest Management, Proceedings of Second Forest Service Remote Sensing Applications Conference, Slidell, Louisiana, 153-162, 1988. |
Myhre et al., “Airborne Video Technology”, Forest Pest Management/Methods Application Group, Fort Collins, CO, pp. 1-6, at least as early as Jul. 30, 2006. |
Norton-Griffiths et al., 1982. “Sample surveys from light aircraft combining visual observations and very large scale color photography”. University of Arizona Remote Sensing Newsletter 82-2:1-4. |
Norton-Griffiths et al., “Aerial Point Sampling for Land Use Surveys”, Journal of Biogeography, 15:149-156, 1988. |
Novak, Rectification of Digital Imagery, Photogrammetric Engineering and Remote Sensing, 339-344, 1992. |
Slaymaker, Dana M., “Point Sampling Surveys with GPS-logged Aerial Videography”, Gap Bulletin No. 5, University of Idaho, http://www.gap.uidaho.edu/Bulletins/5/PSSwGPS.html, 1996. |
Slaymaker, et al., “Madagascar Protected Areas Mapped with GPS-logged Aerial Video and 35mm Air Photos”, Earth Observation magazine, vol. 9, No. 1, http://www.eomonline.com/Common/Archives/2000jan/00jan_tableofcontents.html, pp. 1-4, 2000. |
Slaymaker, et al., “Cost-effective Determination of Biomass from Aerial Images”, Lecture Notes in Computer Science, 1737:67-76, http://portal.acm.org/citation.cfm?id=648004.743267&coll=GUIDE&dl=, 1999. |
Slaymaker, et al., “A System for Real-time Generation of Geo-referenced Terrain Models”, 4232A-08, SPIE Enabling Technologies for Law Enforcement Boston, MA, ftp://vis-ftp.cs/umass.edu/Papers/schultz/spie2000.pdf, 2000. |
Slaymaker, et al.,“Integrating Small Format Aerial Photography, Videography, and a Laser Profiler for Environmental Monitoring”, In ISPRS WG III/1 Workshop on Integrated Sensor Calibration and Orientation, Portland, Maine, 1999. |
Slaymaker, et al., “Calculating Forest Biomass With Small Format Aerial Photography, Videography and a Profiling Laser”, In Proceedings of the 17th Biennial Workshop on Color Photography and Videography in Resource Assessment, Reno, NV, 1999. |
Slaymaker et al., Mapping Deciduous Forests in Southern New England using Aerial Videography and Hyperclustered Multi-Temporal Landsat TM Imagery, Department of Forestry and Wildlife Management, University of Massachusetts, 1996. |
Star et al., “Geographic Information Systems an Introduction”, Prentice-Hall, 1990. |
Tomasi et al., “Shape and Motion from Image Streams: a Factorization Method”—Full Report on the Orthographic Case, pp. 9795-9802, 1992. |
Warren, Fire Mapping with the Fire Mousetrap, Aviation and Fire Management, Advanced Electronics System Development Group, USDA Forest Service, 1986. |
Welch, R., “Desktop Mapping with Personal Computers”, Photogrammetric Engineering and Remote Sensing, 1651-1662, 1989. |
Westervelt, James, “Introduction to GRASS 4”, pp. 1-25, 1991. |
“RGB Spectrum Videographics Report, vol. 4, No. 1, McDonnell Douglas Integrates RGB Spectrum Systems in Helicopter Simulators”, pp. 1-6, 1995. |
RGB “Computer Wall”, RGB Spectrum, 4 pages, 1995. |
“The First Scan Converter with Digital Video Output”, Introducing . . . The RGB/Videolink 1700D-1, RGB Spectrum, 2 pages, 1995. |
Erdas Field Guide, Version 7.4, A Manual for a commercial image processing system, 1990. |
“Image Measurement and Aerial Photography”, Magazine for all branches of Photograrnmetry and its fringe areas, Organ of the German Photogrammetry Association, Berlin-Wilmersdorf, No. 1, 1958. |
“Airvideo Analysis”, Microimages, Inc., Lincoln, NE, 1 page, Dec. 1992. |
Zhu, Zhigang, Hanson, Allen R., “Mosaic-Based 3D Scene Representation and Rendering”, Image Processing, 2005, ICIP 2005, IEEE International Conference on 1(2005). |
Mostafa, et al., “Direct Positioning and Orientation Systems How do they Work? What is the Attainable Accuracy?”, Proceeding, American Society of Photogrammetry and Remote Sensing Annual Meeting, St, Louis, MO, Apr. 24-27, 2001. |
“POS AV” georeferenced by APPLANIX aided inertial technology, http://www.applanix.com/products/posav_index.php. |
Mostafa, et al., “Ground Accuracy from Directly Georeferenced Imagery”, Published in GIM International vol. 14 N. Dec. 12, 2000. |
Mostafa, et at., “Airborne Direct Georeferencing of Frame Imagery: An Error Budget”, The 3rd International Symposium on Mobile Mapping Technology, Cairo, Egypt, Jan. 3-5, 2001. |
Mostafa, M.R. and Hutton, J., “Airborne Kinematic Positioning and Attitude Determination Without Base Stations”, Proceedings, Internationai Symposium on Kinematic Systems in Geodesy, Geomatics, and Navigation (KIS 2001) Benff, Alberta, Canada, Jun. 4-8, 2001. |
Mostafa, et al., “Airborne DGPS Without Dedicated Base Stations for Mapping Applications”, Proceedings of ION-GPS 2001, Salt Lake City, Utah, USA, Sep. 11-14. |
Mostafa, “ISAT Direct Exterior Orientation QA/QC Strategy Using POS Data”, Proceedings of OEEPE Workshop: Integrated Sensor Orientation, Hanover, Germany, Sep. 17-18, 2001. |
Mostafa, “Camera/IMU Boresight Calibration: New Advances and Performance Analysis”, Proceedings of the ASPRS Annual Meeting, Washington, D.C., Apr. 21-26, 2002. |
Hiatt, “Sensor Integration Aids Mapping at Ground Zero”, Photogrammetric Engineering and Remote Sensing, Sep. 2002, p. 877-878. |
Mostafa, “Precision Aircraft GPS Positioning Using CORS”, Photogrammetric Engineering and Remote Sensing, Nov. 2002, p. 1125-1126. |
Mostafa, et al., System Performance Analysis of INS/DGPS Integrated System for Mobile Mapping System (MMS), Department of Geomatics Engineering, University of Calgary, Commission VI, WG VI/4, Mar. 2004. |
Artes F., & Hutton, J., “GPS and Inertial Navigation Delivering”, Sep. 2005, GEOconnexion International Magazine, p. 52-53, Sep. 2005. |
“POS AV” APPLANIX, Product Outline, airborne@applanix.com, 3 pages, Mar. 28, 2007. |
POSTrack, “Factsheet”, APPLANIX, Ontario, Canada, www.applanix.com, Mar. 2007. |
POS AV “Digital Frame Camera Applications”, 3001 Inc., Brochure, 2007. |
POS AV “Digital Scanner Applications”, Earthdata Brochure, Mar. 2007. |
POS AV “Film Camera Applications” AeroMap Brochure, Mar. 2007. |
POS AV “LIDAR Applications” MD Atlantic Brochure, Mar. 2007. |
POS AV “OEM System Specifications”, 2005. |
POS AV “Synthetic Aperture Radar Applications”, Overview, Orbisat Brochure, Mar. 2007. |
“POSTrack V5 Specifications” 2005. |
“Remote Sensing for Resource Inventory Planning and Monitoring”, Proceeding of the Second Forest Service Remote Sensing Applications Conference—Slidell, Louisiana and NSTL, Mississippi, Apr. 11-15, 1988. |
“Protecting Natural Resources with Remote Sensing”, Proceeding of the Third Forest Service Remote Sensing Applications Conference—Apr. 9-13, 1990. |
Heipke, et al, “Test Goals and Test Set Up for the OEEPE Test—Integrated Sensor Orientation”, 1999. |
Kumar, et al., “Registration of Video to Georeferenced Imagery”, Sarnoff Corporation, CN5300, Princeton, NJ, 1998. |
McConnel, Proceedings Aerial Pest Detection and Monitoring Workshop—1994.pdf, USDA Forest Service Forest Pest Management, Northern Region, Intermountain region, Forest Insects and Diseases, Pacific Northwest Region. |
“Standards for Digital Orthophotos”, National Mapping Program Technical Instructions, US Department of the Interior, Dec. 1996. |
Tao, “Mobile Mapping Technology for Road Network Data Acquisition”, Journal of Geospatial Engineering, vol, 2, No. 2, pp. 1-13, 2000. |
“Mobile Mapping Systems Lesson 4”, Lesson 4 SURE 382 Geographic Information Systems II, pp. 1-29, Jul. 2, 2006. |
Konecny, G., “Mechanische Radialtriangulation mit Konvergentaufnahmen”, Bildmessung und Luftbildwesen, 1958, Nr. 1. |
Myhre, “ASPRS/ACSM/RT 92” Technical papers, Washington, D.C., vol. 5 Resource Technology 92, Aug. 3-8, 1992. |
Rattigan, “Towns get new view from above,” The Boston Globe, Sep. 5, 2002. |
Mostafa, et al., “Digital image georeferencing from a multiple camera system by GPS/INS,” ISP RS Journal of Photogramrnetty & Remote Sensing, 56(I):I-12, Jun. 2001. |
Dillow, “Grin, or bare it, for aerial shot,” Orange County Register (California), Feb. 25, 200I. |
Anonymous, “Live automatic coordinates for aerial images,” Advanced Imaging, 12(6):51, Jun. 1997. |
Anonymous, “Pictometry and US Geological Survey announce—Cooperative Research and Development Agreement,” Press Release published Oct. 20, 1999. |
Miller, “Digital software gives small Arlington the Big Picture,” Government Computer NewsState & Local, 7(12), Dec. 2001. |
Garrett, “Pictometry: Aerial photography on steroids,” Law Enforcement Technology 29(7):114-116, Jul. 2002. |
Weaver, “County gets an eyeful,” The Post-Standard (Syracuse, NY), May 18, 2002. |
Reed, “Firm gets latitude to map O.C, in 3D,” Orange County Register (California), Sep. 27, 2000. |
Reyes, “Orange County freezes ambitious aerial photography project,” Los Angeles Times, Oct. 16, 2000. |
Aerowest Pricelist of Geodata as of Oct. 21, 2005 and translations to English 3 pages. |
www.archive.org Web site showing archive of German AeroDach Web Site http://www.aerodach.de from Jun. 13, 2004 (retrieved Sep. 20, 2012) and translations to English 4 pages. |
AeroDach® Online Roof Evaluation Standard Delivery Format and 3D Data File: Document Version 01.00.2002 with publication in 2002, 13 pages. |
Noronha et al., “Detection and Modeling of Building from Multiple Aerial Images,” Institute for Robotics and Intelligent Systems, University of Southern California, Nov. 27, 2001, 32 pages. |
Applicad Reports dated Nov. 25, 1999-Mar. 9, 2005, 50 pages. |
Applicad Online Product Bulletin archive from Jan. 7, 2003, 4 pages. |
Applicad Sorcerer Guide, Version 3, Sep. 8, 1999, 142 pages. |
Xactimate Claims Estimating Software archive from Feb. 12, 2010, 8 pages. |
Bignone et al, Automatic Extraction of Generic House Roofs from High Resolution Aerial Imagery, Communication Technology Laboratory, Swiss Federal Institute of Technology ETH, CH-8092 Zurich, Switzerland, 12 pages, 1996. |
Geospan 2007 Job proposal. |
Greening et al., Commercial Applications of GPS-Assisted Photogrammetry, Presented at GIS/LIS Annual Conference and Exposition, Phoenix, AZ, Oct. 1994. |
Applanix Corp, Robust, Precise Position and Orientation Solutions, POS/AV & POS/DG Installation & Operation Manual, Redefining the way you survey, May 19, 1999, Ontario, Canada. |
Applanix Corp, Robust, Precise Position and Orientation Solutions, POS/AV V4 Ethernet & Disk Logging ICD, Redefining the way you survey, Revision 3, Apr. 18, 2001, Ontario, Canada. |
PCT/US08/62254 Written Opinion of the International Searching Authority dated Sep. 17, 2008. |
PCT/US08/62254 Preliminary Report on Patentability dated Nov. 3, 2009. |
European Patent Office; Supplementary European Search report and European search opinion regarding European Patent Application No. 08754985.3; dated Jul. 5, 2012. |
Applicant; Response to Jul. 5, 2012 Supplementary European search report and European search opinion regarding European Patent Application No. 08754985.3; dated Feb. 4, 2013. |
European Patent Office; Examination Report regarding European Patent Application No. 08754985.3; dated Dec. 16, 2013. |
Applicant; Response to Dec. 16, 2013 Examinatioin Report regarding European Patent Application No. 08754985.3; dated Jun. 26, 2014. |
Mostafa et al., Airborne Remote Sensing Without Ground Control, IGARSS 2001, IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, Australia, Jul. 9-13, 2001, vol. 7, Jul. 9, 2001 (Jul. 9, 2001), pp. 2961-2963. |
USPTO—Hogue, Final Office Action dated Mar. 16, 2009 for U.S. Appl. No. 11/031,505; 28 pages. |
Noguchi, Resubmitted Amendment to USPTO for U.S. Appl. No. 11/031,505; dated Dec. 24, 2009, 17 pages. |
USPTO—Seth, Office Action dated Oct. 10, 2012 for U.S. Appl. No. 13/350,483. |
Applicant, Response to Oct. 10, 2012 Office Action regarding U.S. Appl. No. 13/350,483; dated Apr. 10, 2013. |
USPTO—Seth, Notice of Allowance for U.S. Appl. No. 13/350,483; dated Apr. 25, 2013. |
European Patent Office, Extended European Search Report for European Patent Office patent application No. 18190240.4 dated Dec. 13, 2018. |
Ostendorp, M., “Innovative Airborne Inventory and Inspection Technology for Electric Power Line Condition Assessments and Deft Reporting”, Transmission and Distribution Construction, Operation and Live-Line Maintenance Proceedings. 2000 IEEE ESMO-2000 IEEE9th International Conference on Oct. 9-12, 2000, Piscataway, NJ, USA, IEEE, Oct. 9, 2000 (Oct. 9, 2000), pp. 123-128, XP010522398, ISBN: 978-0-7803-6625-1 *abstract* *third paragraph on p. 125*. |
Meth, et al, “Target Aspect Estimation from Single and Mufti-Pass SAR Images”, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP '98. Seattle, WA, May 12-15, 1998; [IEEE International Conference on Acoustics, Speech and Signal Processing], New York, NY: IEEE, US, vol. CONF. 23, May 12, 1998 (May 12, 1998), pp. 2745-2748, XP)))894887, DOI: 10.1109/ICASSP.1998.678091 ISBN: 978-0-7803-4429-7 *abstract* *Section 1*. |
Pictometry International Corp., Response to European Patent Office Dec. 13, 2018 Search Report and Written Opinion regarding European Patent Application No. 18190240.4, dated Oct. 28, 2019. |
European Patent Office, Examination Report regarding European Patent Application No. 18190240.4, dated May 11, 2020. |
Pictometry International Corp., Response to European Patent Office May 11, 2020 Examination Report regarding European Patent Application No. 18190240.4, dated Sep. 14, 2020. |
Number | Date | Country | |
---|---|---|---|
20200372627 A1 | Nov 2020 | US |
Number | Date | Country | |
---|---|---|---|
60926985 | May 2007 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 16266852 | Feb 2019 | US |
Child | 16892960 | US | |
Parent | 15965086 | Apr 2018 | US |
Child | 16266852 | US | |
Parent | 15493434 | Apr 2017 | US |
Child | 15965086 | US | |
Parent | 15043068 | Feb 2016 | US |
Child | 15493434 | US | |
Parent | 13744174 | Jan 2013 | US |
Child | 15043068 | US | |
Parent | 12112837 | Apr 2008 | US |
Child | 13744174 | US |