Abstract
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.
Highlights
Hyperspectral imagery has been widely investigated for landcover classification due to its broad coverage of wavelength and high spectral sampling rate
In (Tarabalka et al, 2011) an efficient approach was proposed for spectral-spatial classification using the Marker-based Hierarchical Segmentation (MHS) grown from automatically selected markers
This decision making process is continued using other Enhanced MHS algorithms until the answer is negative for the pixel label which is determined by Multi-Layer Perceptron (MLP) algorithm
Summary
Hyperspectral imagery has been widely investigated for landcover classification due to its broad coverage of wavelength and high spectral sampling rate. An alternative way in order to improve the accuracy of segmentation is performing a marker-based technique (Gonzalez and Woods, 2002; Soille, 2003) In this approach for each spatial object of the image, one or several pixels are selected as seed or marker. In (Tarabalka et al, 2011) an efficient approach was proposed for spectral-spatial classification using the Marker-based Hierarchical Segmentation (MHS) grown from automatically selected markers. It uses a pixel-wise SVM classification, in order to select pixels with the highest probability estimate to each class, as markers. The most reliable labeled pixels are selected as the markers
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