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This manuscript proposes a symmetric sparse representation (SSR) method to extract pure endmembers from Hyperspectral imagery (HSI). The SSR assumes that the desired endmembers and all the HSI pixels can be sparsely represented by each other and it formulates the endmember extraction problem into finding archetypes in the minimal convex hull of the HSI data. The optimization program of SSR is solved...
The lack of a comprehensive solution for image information mining has often brought confusion and misunderstanding when Earth Observation data based application scenarios were addressed. Considering the variety of dedicated sensors available nowadays, the particularities of the recorded data raises serious issues when explored. Most of the proposed methodologies for data analysis integrate algorithms...
Building digitalization is an important component of digital city development. Terrestrial laser scanning (TLS) technology provides a high-precision and high-density data source to guarantee building digitalization and reconstruction. However, when these large scattered points cloud are used to build a 3D digital model, the points of building boundaries extraction is an essential issue. In this paper,...
Change detection by unmixing has been shown to provide enhanced change detection performance for hyperspectral images with respect to more traditional approaches, especially when the temporal images contain sub-pixel level changes. In a recent paper, change detection by spectral unmixing was investigated in detail and the advantages that can be gained by using such an approach were systematically...
Sparse unmixing algorithm aims at finding the optimal subset of signatures from a spectral library to best model each pixel in hyperspectral image and estimating their corresponding abundance. However, the high mutual coherence of spectral library limits the performance of sparse unmixing algorithm. In this paper, a method referencing the extracted information from hyperspectral image was managed...
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