A hyperspectral consists of a great number of spectral images. Although these spectral images are obtained
simultaneously, due to a number of optical effects the hyperspectral bands are not co-registered. This has been found to
be particularly prominent in images of regions near the absorption bands of atmospheric gases.
We have developed a method using subpixel image registration that enables us to identify the spatial misregistration
between spectral bands and correct it. We use one band as a reference band and match all other spectral images to this
band. We use the average row and column disparity to correct the spatial misregistration.
Other than the spatial correction that we obtain we also obtain a mapping of the atmospheric gas absorption features.
This is done by plotting the average disparities as a function of wavelength. Local peaks in this plot are clear evidence of
absorption features of the atmospheric gases. Our method is robust to the low snr in the atmospheric absorption images
with low transmission. The correction, results in a hyperspectral cube with all the bands spatially aligned. The algorithm
has been applied to the following hyperspectral imagers : AISA Hawk, AISA Eagle, AVIRIS and HYPERION.
Automatic delineation of buildings is very attractive for both civilian and military applications. Such applications include general mapping, detection of unauthorized constructions, change detection, etc. For military applications, high demand exists for accurate building change updates, covering large areas, and over short time periods. We present two algorithms coupled together. The
height image algorithm is a fast coarse algorithm operating on large areas. This algorithm is capable of defining blocks of buildings and regions of interest. The point-cloud algorithm is a fine, 3D-based, accurate algorithm for building delineation. Since buildings may be
separated by alleys, whose width is similar or narrower than the LADAR resolution, the height image algorithm marks those crowded buildings as a single object. The point-cloud algorithm separates and accurately delineates individual building boundaries and building sub-sections utilizing roof shape analysis in 3D. Our focus is on the ability to cover large areas with accuracy and high rejection of non-building objects, like trees. We report a very good detection performance with only few misses and false alarms. It is believed that LADAR measurements, coupled with good segmentation algorithms, may replace older systems and methods that require considerable manual work for such applications.