Abstract: A method is provided for estimating velocity of a moving object. The method includes generating a raw velocity for the moving object from a position change between a first image generated by a first sensor and a second image generated by a second sensor of a sensor assembly and selecting a plurality of stationary background features that are captured with the moving object in one or more images. A background misregistration value is generated from apparent movement of the plurality of stationary background features and is used to correct the raw velocity.

Abstract: Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.

Abstract: Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.

Abstract: Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.

Abstract: Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.

Abstract: Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a cloud dictionary and a ground dictionary, determining whether a given pixel is best represented by “words” from the cloud dictionary or “words” from the ground dictionary to make an initial determination of cloud or ground, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.

Abstract: Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a cloud dictionary and a ground dictionary, determining whether a given pixel is best represented by “words” from the cloud dictionary or “words” from the ground dictionary to make an initial determination of cloud or ground, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.