Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help mitigate false positives. A material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms is presented. The algorithms utilize unwhitened radiance data and Nelder–Meade numerical optimization to estimate the temperature, humidity, and ozone levels of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian information criteria for model selection. The resulting identification product includes an optimal atmospheric profile and a full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing the model parameters to improve identification is also presented. Several examples are provided using modeled data at several noise levels.
Image registration is used by the remote sensing community to align images for the purposes of examining changes in a scene. The application in this paper involves finding anomalies associated with human activity for the purpose of detecting underground nuclear explosions. This paper presents a non-rigid image registration algorithm that can be easily implemented using publicly available tools such as python, numpy, scipy, openCV and SIFT. SIFT is used to find feature correspondences between images. An approach based on Mahalanobis distance is used find a subset of robust correspondences. Comparisons are made to the RANSAC algorithm. The imagery was collected by DigitalGlobe’s Worldview-II satellite. One image pair is orthorectified. A second image pair is only geo-registered. Both image pairs were collected over mountainous desert regions, the second image pair has much rougher terrain and presents a challenging situation. The non-rigid property of the image registration algorithm allows for robust registration in mountainous terrain under different viewing geometries. Image differencing of the PAN-chromatic band is used to find changes, some of which are shown in detail for both sets of images. Overall registration improvement is quantified by using the standard deviation of the difference image.<p> </p> The non-rigid warping map was also applied to the multispectral bands of the DigitalGlobe data. This dataset made use of a multivariate change detection algorithm that incorporates the spectral properties of each pixel.
The terrestrial remote sensing community is interested in segmented aperture space telescopes with geometries
similar to NASA's James Webb Space Telescope (JWST). However, the unorthodox design has caused a decrease
in image quality introduced by piston, tip, and tilt phasing errors of the segments and lightweight mirror aberrations.
Traditionally image quality has been determined using the Generalized Image Quality Equation (GIQE),
however Fiete et.al.1 have shown that there are inherent problems with the GIQE method when working with
apertures that are not circularly symmetric. In this paper an image utility technique utilizing a multispectral
motion detection algorithm is used to show how changes in mirror phasing and varying degrees of lightweight
mirror aberrations affect a systems utility for detecting motion.
Conference Committee Involvement (1)
Applications of Machine Learning
11 August 2019 | San Diego, California, United States