Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.
Funding Information
  • National Natural Science Foundation of China (61703060, 61802036, 61701048, 61873305, 21YYJC0469, 2020M683274, 2019NQN07, scsxdz2020zd01, csxdz201710, MSSB-2018-0, MSSB-2020-9)
  • Sichuan Science and Technology Program (61703060, 61802036, 61701048, 61873305, 21YYJC0469, 2020M683274, 2019NQN07, scsxdz2020zd01, csxdz201710, MSSB-2018-0, MSSB-2020-9)
  • China Postdoctoral Science Foundation (61703060, 61802036, 61701048, 61873305, 21YYJC0469, 2020M683274, 2019NQN07, scsxdz2020zd01, csxdz201710, MSSB-2018-0, MSSB-2020-9)
  • Fundamental Research Funds of the Central Universities, Southwest Minzu University (61703060, 61802036, 61701048, 61873305, 21YYJC0469, 2020M683274, 2019NQN07, scsxdz2020zd01, csxdz201710, MSSB-2018-0, MSSB-2020-9)
  • Opening Fund of Geomathematics Key Laboratory of Sichuan Province (61703060, 61802036, 61701048, 61873305, 21YYJC0469, 2020M683274, 2019NQN07, scsxdz2020zd01, csxdz201710, MSSB-2018-0, MSSB-2020-9)
  • Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan (61703060, 61802036, 61701048, 61873305, 21YYJC0469, 2020M683274, 2019NQN07, scsxdz2020zd01, csxdz201710, MSSB-2018-0, MSSB-2020-9)

This publication has 43 references indexed in Scilit: