A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization
The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of no...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/4/1042 |
| _version_ | 1797476781644578816 |
|---|---|
| author | Zhongliang Ren Qiuping Zhai Lin Sun |
| author_facet | Zhongliang Ren Qiuping Zhai Lin Sun |
| author_sort | Zhongliang Ren |
| collection | DOAJ |
| description | The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of noise, the mapping accuracy of SM is usually poor, and its per-pixel matching method is inefficient to some extent. To solve these problems, we propose an unsupervised clustering-matching mapping method, using a combination of k-means and SM (KSM). First, nonnegative matrix factorization (NMF) is used and combined with a simple and effective NMF initialization method (SMNMF) for feature extraction. Then, k-means is implemented to get the cluster centers of the extracted features and band depth, which are used for clustering and matching, respectively. Finally, dimensionless matching methods, including spectral angle mapper (SAM), spectral correlation angle (SCA), spectral gradient angle (SGA), and a combined matching method (SCGA) are used to match the cluster centers of band depth with a spectral library to obtain the mineral mapping results. A case study on the airborne hyperspectral image of Cuprite, Nevada, USA, demonstrated that the average overall accuracies of KSM based on SAM, SCA, SGA, and SCGA are approximately 22%, 22%, 35%, and 33% higher than those of SM, respectively, and KSM can save more than 95% of the mapping time. Moreover, the mapping accuracy and efficiency of SMNMF are about 15% and 38% higher than those of the widely used NMF initialization method. In addition, the proposed SCGA could achieve promising mapping results at both high and low signal-to-noise ratios compared with other matching methods. The mapping method proposed in this study provides a new solution for the rapid and autonomous identification of minerals and other fine objects. |
| first_indexed | 2024-03-09T21:08:35Z |
| format | Article |
| id | doaj.art-d2d2995f720e4c9295db26a6ae06a09b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T21:08:35Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d2d2995f720e4c9295db26a6ae06a09b2023-11-23T21:56:04ZengMDPI AGRemote Sensing2072-42922022-02-01144104210.3390/rs14041042A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix FactorizationZhongliang Ren0Qiuping Zhai1Lin Sun2College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, School of Resource and Environmental Sciences, Linyi University, Linyi 276000, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaThe emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of noise, the mapping accuracy of SM is usually poor, and its per-pixel matching method is inefficient to some extent. To solve these problems, we propose an unsupervised clustering-matching mapping method, using a combination of k-means and SM (KSM). First, nonnegative matrix factorization (NMF) is used and combined with a simple and effective NMF initialization method (SMNMF) for feature extraction. Then, k-means is implemented to get the cluster centers of the extracted features and band depth, which are used for clustering and matching, respectively. Finally, dimensionless matching methods, including spectral angle mapper (SAM), spectral correlation angle (SCA), spectral gradient angle (SGA), and a combined matching method (SCGA) are used to match the cluster centers of band depth with a spectral library to obtain the mineral mapping results. A case study on the airborne hyperspectral image of Cuprite, Nevada, USA, demonstrated that the average overall accuracies of KSM based on SAM, SCA, SGA, and SCGA are approximately 22%, 22%, 35%, and 33% higher than those of SM, respectively, and KSM can save more than 95% of the mapping time. Moreover, the mapping accuracy and efficiency of SMNMF are about 15% and 38% higher than those of the widely used NMF initialization method. In addition, the proposed SCGA could achieve promising mapping results at both high and low signal-to-noise ratios compared with other matching methods. The mapping method proposed in this study provides a new solution for the rapid and autonomous identification of minerals and other fine objects.https://www.mdpi.com/2072-4292/14/4/1042hyperspectral mineral mappingclusteringspectral matchingnonnegative matrix factorization |
| spellingShingle | Zhongliang Ren Qiuping Zhai Lin Sun A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization Remote Sensing hyperspectral mineral mapping clustering spectral matching nonnegative matrix factorization |
| title | A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization |
| title_full | A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization |
| title_fullStr | A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization |
| title_full_unstemmed | A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization |
| title_short | A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization |
| title_sort | novel method for hyperspectral mineral mapping based on clustering matching and nonnegative matrix factorization |
| topic | hyperspectral mineral mapping clustering spectral matching nonnegative matrix factorization |
| url | https://www.mdpi.com/2072-4292/14/4/1042 |
| work_keys_str_mv | AT zhongliangren anovelmethodforhyperspectralmineralmappingbasedonclusteringmatchingandnonnegativematrixfactorization AT qiupingzhai anovelmethodforhyperspectralmineralmappingbasedonclusteringmatchingandnonnegativematrixfactorization AT linsun anovelmethodforhyperspectralmineralmappingbasedonclusteringmatchingandnonnegativematrixfactorization AT zhongliangren novelmethodforhyperspectralmineralmappingbasedonclusteringmatchingandnonnegativematrixfactorization AT qiupingzhai novelmethodforhyperspectralmineralmappingbasedonclusteringmatchingandnonnegativematrixfactorization AT linsun novelmethodforhyperspectralmineralmappingbasedonclusteringmatchingandnonnegativematrixfactorization |