An imaging system comprises an imaging platform, a camera operatively connected to the imaging platform, and a controller operatively connected to control the imaging platform and the camera. The controller includes machine readable instructions configured to cause the controller to perform a method.

Techniques are disclosed for systems and methods for water non-water segmentation of navigational imagery to assist in the autonomous navigation of mobile structures. An imagery based navigation system includes a logic device configured to communicate with an imaging module coupled to a mobile structure and/or configured to capture images of an environment about the mobile structure. The logic device may be configured to receive at least one image from the imaging module; determine a water/non-water segmented image based, at least in part, on the received at least one image, and generate a range chart corresponding to the environment about the mobile structure based, at least in part, on the determined water/non-water segmented image and/or the received at least one image.

An image processing device 100 includes a calculation unit 110 for calculating a solar radiation spectrum in a given area on the ground surface on the basis of an observed image of the area, a solar radiation spectrum component of the area, and the spectrum of a pure substance estimated in the area, and a conversion unit 120 for converting the observed image on the basis of the calculated solar radiation spectrum.

The present invention discloses a method for recognizing a seawater polluted area based on a high-resolution remote sensing image and a device and belongs to the field of digital image processing. According to the method, firstly, automatic sea and land classification is performed on a remote sensing image by using a supervised learning algorithm, a classification result may reach a higher precision level by processized iterative clustering, and meanwhile, compared with an existing analysis and classification method for a sea and land boundary, the algorithm is less in calculation; and then, a chlorophyll-associated normalized difference vegetation index, a brightness-associated normalized difference water shadow index, a segmentation-based image interpretation thought and a human visual saliency based mechanism in remote sensing interpretation are combined by virtue of a chlorophyll concentration difference of a seawater polluted area and surrounding seawater and a brightness difference of pollutant shadows, and the seawater polluted area is extracted by threshold segmentation, an area where the water quality is good and a heavily polluted area are respectively extracted, and then, a pollution transition area is further extracted. The method disclosed by the present invention provides convenience and an accurate reference for prevention and control of marine pollution.

A method for identifying a potential landslide hazard of a reservoir bank based on a rock mass degradation feature, includes: determining a remote sensing interpretation identification mark of a potential landslide hazard site induced by rock mass degradation of a hydro-fluctuation belt of a bank slope, and establishing a potential landslide hazard site catastrophe evolution identification model; obtaining an orthoimage of a degradation belt, performing preliminary remote sensing interpretation on the orthoimage, and delineating an area prone to landslide; obtaining an oblique real-scene three-dimensional model of the area prone to landslide by the orthoimage, generating digital elevation model (DEM) data according to the oblique real-scene three-dimensional model for remote sensing fine interpretation, and identifying and extracting the mark; and inputting the mark into the potential landslide hazard site catastrophe evolution identification model to identify a catastrophe evolution mode of the potential landslide hazard site of the degradation belt.

There are provided a method and a system for image segmentation utilizing a GAN architecture. A method for training an image segmentation network according to an embodiment includes: inputting an image to a first network which is trained to output a region segmentation result regarding an input image, and generating a region segmentation result; and inputting the region segmentation result generated at the generation step and a ground truth (GT) to a second network, and acquiring a discrimination result, the second network being trained to discriminate inputted region segmentation results as a result generated by the first network and a GT, respectively; and training the first network and the second network by using the discrimination result. Accordingly, region segmentation performance of a semantic segmentation network regarding various images can be enhanced, and a very small image region can be exactly segmented.

A map fusing method includes receiving a source graph and a target graph. The source graph is representative of a source map and the target graph is representative of a target map and includes nodes and edges that connect the nodes. The method further includes processing each of the source graph and the target graph in a graph convolutional layer to provide graph convolutional layer outputs related to the source graph and to the target graph, processing each of the graph convolutional layer outputs for the source graph and the target graph in a linear rectifying layer to output node feature maps related to the source graph and the target graph. The method further includes selecting pairs of node representations from the node feature maps related to the source graph and the target graph and concatenating the selected pairs to output selected and concatenated pairs of node representations.

The present invention relates to a method of extracting a roof edge image for solar panel installation by using machine learning, the method comprising: a training step for passing original rooftop image data through a second generation unit of an image extraction system to output an image similar to a target image, and passing image data, from which a rooftop edge has been detected, through a first generation unit of the system to identify the image data from an original image; a step for segmenting an obstruction hiding a roof edge, and receiving, by a second discriminator unit, an image in which the roof edge has been detected; a step for optimizing the weight of a parameter, and training the second generation unit and the second discriminator unit again; and a step for automatically connecting edge portions after extracting edges, and generating a complete roof edge image.

A barrier data collection device includes a unit estimation unit that estimates, based on sensor data with position information including height at a time when a mobile body including a flying mobile body moving in the air moves, the sensor data being collected in advance about each of geographical ranges, with an estimator, about each of sets of the geographical ranges and heights included in the sensor data in a predetermined time unit, a barrier state obtained by estimating a state of which of barrier types the set is, a shape estimation unit that estimates, about a set satisfying a condition among the sets, a barrier shape based on the sensor data and an estimation result of the barrier state estimated about each of the sets in the time unit, and a barrier estimation unit that estimates, based on the estimation result of the barrier state estimated about the each of the sets, the estimated barrier shape, and a correct answer ratio of the estimator calculated in advance, a probability for each of the barrier types corresponding to each of the sets and estimates the barrier type corresponding to the set from the estimated probability for each of the barrier types.

A method may include measuring a formation sample using a Raman spectrometer to determine a formation sample characteristic, wherein the formation sample characteristic is mineral ID and distribution, carbon ID and distribution, thermal maturity, rock texture, fossil characterization, or combinations thereof.