The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 449–455, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-449-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 449–455, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-449-2022
 
30 May 2022
30 May 2022

BAND SELECTION OF HYPERSPECTRAL IMAGES BASED ON MARKOV CLUSTERING AND SPECTRAL DIFFERENCE MEASUREMENT FOR OBJECT EXTRACTION

T. Zhang1,3, P. Li1,2,3, Y. Ding1,3, D. Luo1,3, Z. Ma1,3, X. Li1,3, and L. Wen1,3 T. Zhang et al.
  • 1Chongqing Geomatics and Remote Sensing Center, Chong 401147, China
  • 2School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
  • 3Key Laboratory of Monitoring, Evaluation and Early Warning of Territorial Spatial Planning Implementation, Ministry of Natural Resources, Chong 401147, China

Keywords: Band selection, Hyperspectral image, Markov clustering, Spectral difference measurement, JS divergence, Random forest

Abstract. For the existing hyperspectral image (HSI) band selection (BS) algorithm does not consider the strong correlation between adjacent bands and does not meet the high-precision extraction of single target, a HSI BS algorithm based on Markov clustering and target land-type spectral difference measurement is proposed in this paper. Specifically, when using Markov clustering for band clustering, the inter band correlation information is embedded and the noise or bad channel band breakpoint is set to adaptively divide the optimal band clustering subset. Then, in each cluster, based on the band difference under the supervision of target category, an evaluation criterion function is designed to select the optimal band combination for single target object extraction. The BS algorithm proposed in this paper is called MCLSD for short. Taking ZY-1 02D HSI in Tongnan of Chongqing as experimental data, taking cultivated land as extraction object and the Random Forest as classifier, the classification accuracy of the selected bands is evaluated. In addition, the MCLSD is compared with the improved sparse subspace clustering (ISSC) (Sun et al., 2015), orthogonal projection band selection (OPBS) (Zhang et al., 2018) and sparse nonnegative matrix factorization (SNMF) (Qin and et al., 2015). Experimental results show that the MCLSD algorithm can select the most suitable band for cultivated land extraction and achieve higher classification accuracy. Especially when the number of bands is less than 5, the MCLSD algorithm has significant advantages over ISSC, OPBS and SNMF. So the MCLSD BS method can meet the demand of the high-precision extraction of target features from HSI data.