ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-IV-4-W4-43-2017 A NEW SPECTRAL-SPATIAL FRAMEWORK FOR CLASSIFICATION OF HYPERSPECTRAL DATA Akbari D. 1 Surveying and Geomatics Engineering Department, College of Engineering, University of Zabol, Zabol, Iran 13 11 2017 IV-4/W4 43 46 Copyright: © 2017 D. Akbari 2017 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/IV-4-W4/43/2017/isprs-annals-IV-4-W4-43-2017.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/IV-4-W4/43/2017/isprs-annals-IV-4-W4-43-2017.pdf

In this paper, an innovative framework, based on both spectral and spatial information, is proposed. The objective is to improve the classification of hyperspectral images for high resolution land cover mapping. The spatial information is obtained by a marker-based Minimum Spanning Forest (MSF) algorithm. A pixel-based SVM algorithm is first used to classify the image. Then, the marker-based MSF spectral-spatial algorithm is applied to improve the accuracy for classes with low accuracy. The marker-based MSF algorithm is used as a binary classifier. These two classes are the low accuracy class and the remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. To evaluate the proposed approach, the Berlin hyperspectral dataset is tested. Experimental results demonstrate the superiority of the proposed method compared to the original MSF-based approach. It achieves approximately 5 % higher rates in kappa coefficients of agreement, in comparison to the original MSF-based method.