Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10849635/ |
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| Summary: | 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. |
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| ISSN: | 1939-1404 2151-1535 |