Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix

作     者：Wang, Maozhi Chen, Shu-Hua Feng, Jun Xu, Wenxi Wang, Daming 

作者机构：Chengdu Univ Technol Coll Math Sci Geomath Key Lab Sichuan Prov Chengdu 610059 Peoples R China Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA China Geol Survey Tianjin Ctr Tianjin 300170 Peoples R China 

出 版 物：《IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING》 (IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.)

年 卷 期：2025年第18卷

页      面：4751-4766页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0705[理学-地理学] 0816[工学-测绘科学与技术] 

基　　金：National Key R&D Program of China [2022YFB3902000] National Natural Science Foundation of China Natural Science Foundation of Sichuan Province of China [2024NSFSC0083] U.S. National Science Foundation 

主　　题：Classification algorithms Mathematical models Landsat Hyperspectral imaging Training Support vector machines Reviews Linear systems Data models Classification tree analysis Condition number multispectral imagery spectrally similar materials identification supplementary features 

摘      要：Identification of spectrally similar materials from multispectral remote sensing (RS) imagery with only several bands is an important issue that challenges comprehensive applications of the RS of surface characteristics. This study proposes a new method to identify spectrally similar materials from these types of imagery. The method is constructed based on the theory of condition number of matrix, and a theorem is proven as the foundation of the designed identification algorithm. Mathematically, the motivation behind designing this new algorithm is to decrease the condition number of the matrix for a linear system and, by doing so, to change an ill-conditioned system to a well-conditioned one. Technically, this new method achieves the purpose by adding supplementary features to all the original spectra including similar materials, which can be further used as indicative signatures to identify these materials. Thus, the proposed method is named a condition number-based method with supplementary features (SF-CNM). The threshold scheme and supplementary features are two main novelty techniques to ensure the uniqueness and accuracy of the proposed SF-CNM for specified samples. The results for a case study to identify water, ice, snow, shadow, and other materials from Landsat 8 OLI data indicate that SF-CNM can identify the materials specified by the given samples successfully and accurately and that SF-CNM significantly outperforms those of spectral angle mapper algorithm, Mahalanobis classifier, maximum likelihood, and artificial neural network, and produces the performance similar to, even slightly better than that of support vector machine.