Refining a rational functional model (RFM) by subdividing the multiple images into multiple groups of sub-images according to the imaging sensor, then performing the RFM refinement for each group separately and estimating a 3D transformation using the sub-images from the central sensor as a reference.

Systems, methods and apparatus for image processing for reconstructing a super resolution image from multispectral (MS) images. Receive image data and initialize a fused image using a panchromatic (PAN) image, and estimate a blur kernel between the PAN image and the MS images as an initialization function. Iteratively, fuse a MS image with an associated PAN image of a scene using a fusing algorithm. Each iteration includes: update the blur kernel based on a Second-Order Total Generalized Variation function to regularize a kernel shape; fuse the PAN image and MS images with the updated blur kernel based on a local Laplacian prior function to regularize the high-resolution information to obtain an estimated fused image; compute a relative error between the estimated fused image of the current iteration and a previous estimated fused image from a previous iteration, to a predetermined threshold, to stop iterations stop, to obtain a PAN-sharpened image.

A land analysis system uses drone-captured images to detect plant health and/or soil moisture levels at a site. For example, the system instructs a drone to fly along a flight path, capture images of the land below, and measure altitude data. The system processes the images using, for example, artificial intelligence, to identify locations at which plant material may be present. The system then further processes the images to identify the plant health of the plant material at the identified locations. The system further uses the altitude data to determine the strata of plants at the identified locations. Optionally, the system can further process the images to identify the soil moisture levels at the identified locations.

A method for increasing spatial resolution of a MS image using a PAN image. For a portion of the scene, values of parameters of a scene model are obtained according to a resemblance between a simulated MS reflectance and the MS reflectance. A relative variation in the simulated MS reflectance is determined with respect to a simulated PAN reflectance near the values of parameters obtained. A difference between the PAN reflectance and a reflectance of a PAN image with reduced spatial resolution is estimated. An MS image with increased spatial resolution is determined, by adding to the MS reflectance a correction corresponding to a product of this difference and this relative variation. A corresponding image-processing system is also provided.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s). Replacement pixel data may be calculated based on the unobscured pixels and used to generate a transient-obstruction-free version of the digital image.

Systems and methods for multiple image registration of images of a scene or an object. Receiving image data, the image data includes images collected from different measurements of a single modality or multiple modalities, either at different rotation angles, horizontal shifts, or vertical shifts, of the scene or the object. Estimating registration parameters, using pairs of images, each pair of images includes a reference image and a floating image. Generating parameter matrices corresponding to registration parameters using an image registration process for all pairs of images. Decomposing each parameter matrix into a low-rank matrix of updated registration parameters and a sparse matrix corresponding to the registration parameter errors for each low-rank matrix, to obtain updated registration parameters for robust registration. Using the updated registration parameters to form a transformation matrix to register the images with at least one reference image, resulting in robust registration of the images.

Described herein is a method of generating an image that includes receiving a set of data corresponding to an object. The method also includes generating a three-dimensional representation of the object using the set of data. The method includes generating properties for the object using the set of data. The method also includes associating the properties with the three-dimensional representation of the object, wherein the three-dimensional representation of the object and the properties for the object are used to produce training data for an object recognition algorithm.

A method of operating a dimensioning system to determine dimensional information for objects is disclosed. A number of images are acquired. Objects in at least one of the acquired images are computationally identified. One object represented in the at least one of the acquired images is computationally initially selected as a candidate for processing. An indication of the initially selected object is provided to a user. At least one user input indicative of an object selected for processing is received. Dimensional data for the object indicated by the received user input is computationally determined.

Disclosed are a method and apparatus for SAR image data enhancement, and a storage medium. The method includes: processing an SAR target image by electromagnetic simulation to acquire an SAR electromagnetic simulation image; and processing the SAR electromagnetic simulation image and the SAR target image by a generative adversarial network to obtain a set of virtual samples of the SAR target image.

Implementations relate to detecting/replacing transient obstructions from high-elevation digital images. A digital image of a geographic area includes pixels that align spatially with respective geographic units of the geographic area. Analysis of the digital image may uncover obscured pixel(s) that align spatially with geographic unit(s) of the geographic area that are obscured by transient obstruction(s). Domain fingerprint(s) of the obscured geographic unit(s) may be determined across pixels of a corpus of digital images that align spatially with the one or more obscured geographic units. Unobscured pixel(s) of the same/different digital image may be identified that align spatially with unobscured geographic unit(s) of the geographic area. The unobscured geographic unit(s) also may have domain fingerprint(s) that match the domain fingerprint(s) of the obscured geographic unit(s). Replacement pixel data may be calculated based on the unobscured pixels and used to generate a transient-obstruction-free version of the digital image.