Abundance estimation is an important step of quantitative analysis of hyperspectral remote sensing data. Due to physical interpretation, sum-to-one and non-negativity constraints are generally imposed on the abundances of materials. This paper presents a geometric approach to fully constrained linear spectral unmixing using variable endmember sets for the pixels. First, an improved method for s...

The integration of spatial and spectral responses in hyperspectral image data analysis has been identified as a desirable objective by the remote sensing community. However, most available attempts are based on the consideration of spectral information separately from spatial information, and thus the two types of information are not treated simultaneously. In this paper, we describe our backgr...

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improve data storage, transmission, and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. The spectral adaptive grouping distributed compressive sensing (AGDCS) algorithm is proposed, which enables...

The advent of hyperspectral image technology is a major leap in recent years, it obtains the surface of the earth image contains rich space, radiation and spectral information, Mixed pixels not only effects identification and classification precision of object, but also greatly hinder the development of quantitative remote sensing, so effectively interpret mixed pixels is an important problem f...

Maximum likelihood supervised classifications with 1-m 128 band hyperspectral data accurately map in-stream habitats in the Lamar River, Wyoming with producer’s accuracies of 91% for pools, 87% for glides, 76% for riffles, and 85% for eddy drop zones. Coarser resolution 5-m hyperspectral data and 1-m simulated multiband imagery yield lower accuracies that are unacceptable for inventory and anal...

In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The exploits the mean-shift clustering algorithm that takes as input a preliminary superpixels segmentation together with spectral pixel information. proposed does not require number of classes parameter, and it exploit any a-priori knowledge about type land-cover or land-use to be segmented (e...