Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 133-136, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-133-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 133-136, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-133-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

A HYPERSPECTRAL IMAGE CLASSIFICATION METHOD USING ISOMAP AND RVM

H. Chang, T. Wang, H. Fang, and Y. Su H. Chang et al.
  • The Xi’an Research Institute of High Technology, Xi’an 710025, China

Keywords: Hyperspectral Image Classification, Manifold Learning, Isometric Feature Mapping, Support Vector Machines, Relevance Vector Machines

Abstract. Classification is one of the most significant applications of hyperspectral image processing and even remote sensing. Though various algorithms have been proposed to implement and improve this application, there are still drawbacks in traditional classification methods. Thus further investigations on some aspects, such as dimension reduction, data mining, and rational use of spatial information, should be developed. In this paper, we used a widely utilized global manifold learning approach, isometric feature mapping (ISOMAP), to address the intrinsic nonlinearities of hyperspectral image for dimension reduction. Considering the impropriety of Euclidean distance in spectral measurement, we applied spectral angle (SA) for substitute when constructed the neighbourhood graph. Then, relevance vector machines (RVM) was introduced to implement classification instead of support vector machines (SVM) for simplicity, generalization and sparsity. Therefore, a probability result could be obtained rather than a less convincing binary result. Moreover, taking into account the spatial information of the hyperspectral image, we employ a spatial vector formed by different classes’ ratios around the pixel. At last, we combined the probability results and spatial factors with a criterion to decide the final classification result. To verify the proposed method, we have implemented multiple experiments with standard hyperspectral images compared with some other methods. The results and different evaluation indexes illustrated the effectiveness of our method.