A system and method for generating soil moisture data from satellite images of a geographical area using a deep learning model 108 is provided. The system includes one or more satellites 102A-C, a soil moisture data generator server 106. The method includes, (i) receiving, by a soil moisture data generator server, satellite images of the geographical area, (ii) pre-processing first set of satellite images, second set of satellite images, and third set of satellite images, (iii) interpolating, using spline interpolation, pre-processed first set of images, pre-processed second set of images, and pre-processed third set of images to generate high-resolution set of images, (iv) generating hydrological parameters from the high-resolution set of images, (v) training, a deep learning model, by providing historical hydrological parameters and historical soil moisture data associated with historical satellite images as training data to generate trained deep learning model, (v) generating soil moisture data on daily basis.

Apparatus and method configured to determine locations of man-made objects within synthetic aperture radar (SAR) imagery. The apparatus and method prescreen SAR imagery to identify potential locations of man-made objects within SAR imagery. The potential locations are processed using a change detector to remove locations of natural objects to produce a target image containing location of substantially only man-made objects.

A computing system including a processor configured to train a synthetic aperture radar (SAR) classifier neural network. The SAR classifier neural network is trained at least in part by, at a SAR encoder, receiving training SAR range profiles that are tagged with respective first training labels, and, at an image encoder, receiving training two-dimensional images that are tagged with respective second training labels. Training the SAR classifier neural network further includes, at a shared encoder, computing shared latent representations based on the SAR encoder outputs and the image encoder outputs, and, at a classifier, computing respective classification labels based on the shared latent representations. Training the SAR classifier neural network further includes computing a value of a loss function based on the plurality of first training labels, the plurality of second training labels, and the plurality of classification labels and performing backpropagation based on the value of the loss function.

A noise suppression method and system for Inverse Synthetic Aperture Radar micro-cluster objects using a generative adversarial network (GAN) are provided. The method includes: constructing the GAN, including a generator and a discriminator; obtaining and inputting noisy simulation data into the generator to obtain a first output, comparing the first output with noiseless simulation data to obtain a first generator loss, inputting the first output and the distribution function into the discriminator for denoising discrimination to obtain a first discriminant result, and determining a second generator loss according to the first generator loss and the first discriminate result; and obtaining measured data and inputting the measured data into the generator to obtain a second output, inputting the second output to the discriminator to obtain a second discriminant result, and determining a generator loss according to the second generator and the second discriminate result.

The present disclosure relates to a computer implemented method (10) for detecting an oil slick in a target image acquired by a spaceborne or airborne radar, wherein said method comprises: a phase (T1) of training a convolutional network using a set of training images, the set of training images comprising training images without oil slicks and training images with oil slicks, a phase (T2) of predicting the presence or absence of an oil slick on the target image by applying the convolutional network on said target image, wherein the phase (T1) of training of the convolutional network uses a loss function which combines a weighted-cross-entropy loss function and a Jaccard loss function.

An electromagnetic wave imaging method, system, and apparatus are provided. The method includes collecting an electromagnetic echo signal, where the electromagnetic echo signal is used to indicate electromagnetic wave scattering feature information of a target object, obtaining location information of a reception point of the electromagnetic echo signal, where the location information indicates relative location information between the reception point and a positioning label, and performing electromagnetic wave imaging on the target object based on the electromagnetic wave scattering feature information and the location informati

Methods and apparatuses disclosed within provide a solution to problems associated with sensing apparatus of an automated vehicle (AV) not being able to adequately evaluate risks associated with objects that are not characteristic with a given driving environment. A sensing apparatus tuned to drive at quickly down highway may not adequately identify risks associated with pedestrians walking along that highway. A solution to this problem involves configuring a sensing apparatus to operate in two different modes, for example, a highway mode and a pedestrian mode at virtually the same time. This may include allocating a first percentage of processing resources to a highway mode and allocating a second percentage of processing resources to a pedestrian mode. When objects along the highway are consistent with a pedestrian, additional processing resources of the sensing apparatus may be allocated to the pedestrian mode to reduce a risk of impacting the pedestrian.

A method for detecting radial deformation in a winding of a transformer may include synthetic aperture radar (SAR) imaging of the winding using ultra high frequency (UHF) electromagnetic signals in a first instance of the winding to obtain a first image of the winding; SAR imaging of the winding using UHF electromagnetic signals in a second instance of the winding to obtain a second image of the winding; and comparing the first image of the winding and the second image of the winding to detect a radial deformation in the winding. The UHF electromagnetic signals may be transmitted as a plurality of successive sinusoidal signals, where frequencies of the successive sinusoidal signals gradually change from a first frequency to a second frequency.

An object is to provide an image processing apparatus capable of appropriately distinguishing various object types. An image processing apparatus (1C) comprising: detector means (11) for detecting objects in an input SAR image and generating object chips; projection calculator means (12) for calculating projection information of each object using SAR geometry; feature learner means (14) for learning, for each object, a relation between an object chip and its projection information, and thereby generating learnt features of object chips; and classifier means (15) for classifying object chips into classes based on the learnt features of object chips.

A method for automatic target recognition in synthetic aperture radar (SAR) data, comprising: capturing a real SAR image of a potential target at a real aspect angle and a real grazing angle; generating a synthetic SAR image of the potential target by inputting, from a potential target database, at least one three-dimensional potential target model at the real aspect angle and the real grazing angle into a SAR regression renderer; and, classifying the potential target with a target label by comparing at least a portion of the synthetic SAR image with a corresponding portion of the real SAR image using a processor.