N-FINDER for Finding Endmembers in Compressively Sensed Band Domain
| Autor: | C. J. Della Porta, Chein-I Chang, Chao-Cheng Wu, Adam Bekit, Bernard Lampe |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Vertex (graph theory)
Endmember Computer science Dimensionality reduction 0211 other engineering and technologies Hyperspectral imaging 02 engineering and technology Matrix decomposition Compressed sensing General Earth and Planetary Sciences Electrical and Electronic Engineering Algorithm 021101 geological & geomatics engineering Sparse matrix Curse of dimensionality |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing. 58:1087-1101 |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2019.2943448 |
| Popis: | N-finder algorithm (N-FINDR) has been widely used for finding endmembers in hyperspectral imagery. Since N-FINDR must find all endmembers simultaneously, it requires exhausting all possible $p$ -endmember combinations among the entire data samples with $p$ being the number of endmembers required to be found. Accordingly, directly implementing N-FINDR is practically infeasible. To mitigate this dilemma, two recently developed algorithms called sequential N-FINDR (SQ N-FINDR) and successive N-FINDR (SC N-FINDR) were developed. However, even such an exhaustive search issue can be resolved numerically, another challenging issue for N-FINDR, which remains unsolved, is spectral dimensionality reduction. Because a $p$ -vertex simplex is embedded in a ( $p-1$ )-dimensional spectral data space, N-FINDR does not require full spectral dimensionality to calculate simplex volume (SV). This article presents a compressive sensing (CS) approach to N-FINDR that can find a $p$ -vertex simplex with the maximal SV by SQ/SC N-FINDR in a compressively sensed band domain (CSBD). In particular, to make this idea work, a new CS-based property called restricted SV property (RSVP) can be shown to be preserved in CSBD via a sensing matrix. It is this property that allows what N-FINDR and SQ/SC N-FINDR can achieve in the original data space (ODS) to be also achieved in CSBD. To further show the utility of SQ/SC N-FINDR in both ODS and CSBD as well as SV preserved by RSVP, a series of experiments are conducted for performance analysis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|