Abstract
Abstract. Hyperspectral Images are worthwhile data for many processing algorithms (e.g. Dimensionality Reduction, Target Detection, Change Detection, Classification and Unmixing). Classification is a key issue in processing hyperspectral images. Spectral-identification-based algorithms are sensitive to spectral variability and noise in acquisition. There are many algorithms for classification. 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 Quebec City hyperspectral dataset, demonstrate that the proposed approach achieves approximately 9% and 5% better overall accuracy than the MLP and the original MHS algorithms respectively.
Highlights
Hyperspectral imagery has been widely investigated for landcover classification due to its broad coverage of wavelength and high spectral sampling rate
Methods that can exploit the spatial information are essential for more accurate classification results (Carleer and Wolff, 2006;Shackelford and Davis, 2003).Many researchers have demonstrated that the use of spectral-spatial information improves the classification results, compared to the use of spectral data alone, in hyperspectral imagery (Argüello and Heras, 2015; Blaschke et al, 2014; Fauvel et al, 2012; Huang and Zhang, 2011; Negri et al, 2014; Paneque-Gálvez et al, 2013; Tarabalka et al, 2010).In the early studies on these methods, the spectral information from the neighborhoods is extracted by either a fixed size window (Camps-Valls et al, 2006) or morphological profiles (Fauvel et al, 2008), and used for classifying and labeling of image pixels
This decision making process is continued using other Enhanced Marker-based Hierarchical Segmentation (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.
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