Volume XLII-2/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 1-5, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-1-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 1-5, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-1-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 May 2019

09 May 2019

SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION

D. Akbari1, M. Moradizadeh2, and M. Akbari3 D. Akbari et al.
  • 1Department of Surveying and Geomatics Engineering, College of Engineering, University of Zabol, Zabol, Iran
  • 2Department of Geomatics, Faculty of Civil and Transportation Engineering, University of Isfahan, Isfahan, Iran
  • 3Department of Civil Engineering, College of Engineering, University of Birjand, Birjand, Iran

Keywords: Remote sensing, Hyperspectral image, neural network, Hierarchical segmentation, Marker selection

Abstract. This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.