Update of the Three Dimensional Version of RHSeg and HSeg

Currently, HSEG software is being used by Bartron Medical Imaging as a diagnostic tool to enhance medical imagery. Bartron Medical Imaging licensed the HSEG Technology from NASA Goddard adding color enhancement and developing MED-SEG, an FDA approved tool to help specialists interpret medical images. HSEG is available for licensing outside of the medical field (specifically for soft-tissue analysis).

NASAs Goddard Space Flight Centers method for the extraction of Lunar data and/or planetary features is a method developed to extract Lunar features based on the combination of several image processing techniques. The technology was developed to register images from multiple sensors and extract features from images in low-contrast and uneven illumination conditions. The image processing and registration techniques can include, but is not limited to, a watershed segmentation, marked point processes, graph cut algorithms, wavelet transforms, multiple birth and death algorithms and/or the generalized Hough Transform.

NASA's GONASA technology is a mathematical formula / algorithm built around creating a grid composed of equal-area cells that span the entire visible hemisphere of a spherical object. Traditional longitude and latitude grids produce cells that diminish in size toward the poles due to convergence of longitudinal lines. GONASA circumvents this problem by carefully adjusting the latitude increments, resulting in a network of truly equal-area cells. This adjustment ensures that any feature observed on the spherical surface is accurately represented, regardless of its location. To implement GONASA, the spherical surface is first segmented into discrete latitude bands or rings, each chosen to encompass an identical surface area. Within each ring, longitude divisions maintain equal cell areas, creating a uniform Cartesian grid. The result is a consistent, distortion-corrected matrix suitable for automatic computation, enabling simplified, efficient, and accurate measurements of spatial characteristics such as feature area, centroid location, perimeter, compactness, orientation, and aspect ratio. GONASA grids are computationally efficient and readily adaptable to a range of data processing workflows, from spreadsheets to sophisticated data analysis frameworks like Pandas data frames in Python. Due to their consistent cell sizing and straightforward indexing, GONASA grids facilitate automation, enabling rapid, high-volume data processing and analysis, essential for modern remote sensing and planetary missions that require immediate, reliable data analysis in limited-bandwidth communications environments. At NASA, GONASA has already been successfully implemented to study images of Titan (e.g., mapping its clouds) taken by the Cassini space probe.

The High-Resolution Commercial Validation Products API generates VHR products to support scientists and missions that include two primary foci. The first focus is on-demand VHR regional mosaics. Systematic ortho-rectified and co-registered multi-temporal, panchromatic and unsharpened multi-spectral imagery compiled as user defined regional mosaics will allow for spatially continuous and temporally consistent reference. Such reference will provide an easily accessible calibration and evaluation dataset for scientists. Surface reflectance data from VHR imagery is derived using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and compared over pseudo-invariant calibration sites for cross-calibration to minimize the effects of topography, view angle, date and time of day of collection. The API includes a process for mosaicking and normalizing ortho-rectified images to create scientific data products useful for many programmatic activities, including biodiversity, tree canopy closure, surface water fraction, and cropped area for smallholder agriculture. The second focus is On-Demand VHR digital elevation models (DEMs). Systematic processing of available along- and cross-track stereo VHR imagery is used to produce VHR DEMs. A systematic DEM co-registration approach has been applied to generate products with horizontal and vertical accuracy that can support missions and a number of different science programs. These include studies on the cryosphere, hydrology, and the biosphere.

This suite of technologies includes a method, algorithms, and computer processing techniques to provide for image photometric correction and resolution enhancement at video rates (30 frames per second). This 3D (2D spatial and range) resolution enhancement uses the spatial and range information contained in each image frame, in conjunction with a sequence of overlapping or persistent images, to simultaneously enhance the spatial resolution and range and photometric accuracies. In other words, the technologies allows for generating an elevation (3D) map of a targeted area (e.g., terrain) with much enhanced resolution by blending consecutive camera image frames. The degree of image resolution enhancement increases with the number of acquired frames.