The present invention relates to the field of identifying sea surface anomalies from satellite-acquired and/or airborne-acquired imagery, by means of a backpropagation-enabled method for identifying the sea surface anomalies.
Certain conditions on or near the sea surface create anomalies that can be captured by satellite images. However, there remains a need for a method to efficiently scan satellite images to identify and locate the sea surface anomaly.
The sea surface around the globe is immense, as is the availability of satellite images. Often, satellite images are reviewed for a targeted area to observe changes in the sea surface. But, it is difficult to find sea surface anomalies over vast areas.
CN105630882 relates to an offshore contaminants recognition and tracking system. The system is divided into an application layer, a content analysis and mining layer, a data integration layer resources, resource acquisition layer, comprising pollutants target identification, decision support subsystems, alarm subsystems, pollutants drift forecast subsystem, all kinds of pollution and the chemical composition of product hazards database, clean-up relief material/equipment performance and inventory database, geographic information systems, pollution emergency response capacity evaluation subsystem, subsystems pollution damage assessment can be combined with wireless communication systems technology for emergency response, visual information communication between the aircraft and Coast Guard vessels operating at sea and, according to the report of Coast Guard aircraft, rescue quickly generate, Clear program, directing clean-up boats were a number of clean-up operations integrated marine clean-up technology, quickly and accurately.
There is a need for a backpropagation-enabled method for identifying a sea surface anomaly from satellite-acquired and/or airborne images.
According to one aspect of the present invention, there is provided a backpropagation-enabled method for identifying a sea surface anomaly from satellite-acquired images, airborne images and combinations thereof, comprising the steps of collecting an initial set of images selected from the group consisting of satellite-acquired images, simulated satellite images, and combinations thereof; labeling the anomaly on the initial set of images; using the labels to train a model via backpropagation; collecting a subsequent set of images selected from the group consisting of satellite-acquired, airborne images, and combinations thereof; and applying the trained model to identify a sea surface anomaly on the subsequent set of images.
The present invention will be better understood by referring to the following detailed description of preferred embodiments and the drawings referenced therein, in which:
The method of the present invention trains a model for identifying a sea surface anomaly from satellite-acquired images, airborne-acquired images and a combination thereof. The method of the present invention can identify sea surface anomalies from a set of unlabeled images.
A sea surface anomaly is a deviation in the sea surface relative to the surrounding sea surface, including, for example, without limitation, a wave-damping effect, an optical anomaly, and the like. An example of an optical anomaly is a sheen. Various factors may cause the sea surface anomaly. Substances or structures that may cause a sea surface anomaly include, for example, without limitation, a man-made object, a debris path, a natural or man-made fluid, an underlying formation, such as a coral reef, the presence of hydrocarbons, and the like. Hydrocarbons on a sea surface cause a wave-damping effect and/or a sheen effect that is different from the surrounding sea surface.
In a preferred embodiment of the present invention, the sea surface anomaly is caused by the presence of hydrocarbons, including, for example, without limitation, crude oil and/or refined hydrocarbons. The anomaly may be caused by hydrocarbon seepage from the subsurface, for example, from a natural seepage or from a seepage caused by a man-made action. The anomaly may also be caused by a leak of uncontained hydrocarbons from a man-made facility. The leak may be the direct and/or indirect result of a human action.
Referring now to
Sea surface anomalies in the initial set of images 12 are labeled such that any pixel(s) defined to be part of a sea surface anomaly are identified. The sea surface anomalies may be labeled by a variety of techniques, including, but not limited to, segmentation, localization, classification, and combinations thereof. Segmentation may include generating a custom-polygon around a spatially contiguous sea surface anomaly and/or labeling pixels. In the embodiment shown in
The set of labels 30 are used to train a model via backpropagation.
Examples of backpropagation-enabled processes include, without limitation, artificial intelligence, machine learning, and deep learning. It will be understood by those skilled in the art that advances in backpropagation-enabled processes continue rapidly. The method of the present invention is expected to be applicable to those advances even if under a different name. Accordingly, the method of the present invention is applicable to the further advances in backpropagation-enabled process, even if not expressly named herein.
A preferred embodiment of a backpropagation-enabled process is a deep learning process, including, but not limited to a convolutional neural network.
As depicted generally in
A subsequent set of images 42, illustrated in
In one embodiment of the present invention, the subsequent set of images 42 is acquired from a satellite 44 and transmitted through a satellite receiver 46. The satellite 44 and the satellite receiver 46 may each be the same as or different than the satellite 14 and the satellite receiver 46 used in the step of collecting an initial set of images (shown in
In another embodiment of the present invention, the subsequent set of images 42 is airborne-acquired. Airborne-acquired images may be acquired using, for example, without limitation, an aircraft 54 (depicted in
The subsequent set of images 42 are transmitted to processor 48, depicted generally in
As illustrated in
In a preferred embodiment, the position coordinates are determined for the sea surface anomaly. Position coordinates include, for example, without limitation, a global coordinate reference system.
The method of the present invention is particularly suitable for hydrocarbon-based sea surface anomalies. Once identified, hydrocarbon-based sea surface anomalies identified in accordance with the method of the present invention may be used to locate the source of the hydrocarbons potentially suitable for exploitation or remediation.
In a preferred embodiment, the model may be trained to distinguish between types of hydrocarbons by distinguishing features of the sea surface anomaly. In this way, an output of the method of the present invention may include information about the chemical composition of a hydrocarbon-based sea surface anomaly. For example, the model may be trained to distinguish between a crude oil and a refined petroleum.
While preferred embodiments of the present invention have been described, it should be understood that various changes, adaptations and modifications can be made therein within the scope of the invention(s) as claimed below.
Filing Document | Filing Date | Country | Kind |
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PCT/US2019/057608 | 10/23/2019 | WO | 00 |
Number | Date | Country | |
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62750569 | Oct 2018 | US |