In this article, an innovative classification framework for hyperspectral image data, based on both spectral and spatial information, is proposed. The main objective of this method is to improve the accuracy and efficiency of high-resolution land-cover mapping in urban areas. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MMSF) algorithm. A pixel-based support vector machine (SVM) algorithm is first used to classify the hyperspectral image data, then the enhanced MMSF algorithm is applied in order to increase the accuracy of less accurately classified land-cover types. The enhanced MMSF algorithm is used as a binary classifier. These two classes are the low-accuracy class and remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. In the proposed approach, namely MSF-SVM, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithms, and are then used to build the MSF. Three benchmark hyperspectral data sets are used for the assessment: Berlin, Washington DC Mall, and Quebec City. Experimental results demonstrate the superiority of the proposed approach compared with SVM and the original MMSF algorithms. It achieves approximately 5, 6, and 7% higher rates in kappa coefficients of agreement in comparison with the original MMSF algorithm for the Berlin, Washington DC Mall, and Quebec City data sets, respectively.

This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. The spatial information is obtained by an enhanced marker‐based hierarchical segmentation (MHS) algorithm. The weighted genetic (WG) algorithm is first employed to obtain the subspace of hyperspectral data. The obtained features are then fed into the multi‐layer perceptron (MLP) neural network classification algorithm. Afterwards, the MHS algorithm is applied in order to increase the accuracy of less‐accurately classified land‐cover types. In the proposed approach, namely MLP‐MHS, the markers are extracted from the classification maps obtained by MLP and support vector machine classifiers. Experiments on two benchmark hyperspectral datasets, Pavia University and Berlin, validate the soundness of the proposed approach compared to the MLP and the original MHS algorithms.

Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. Recently, an effective approach for spatial-spectral classification has been proposed using Minimum Spanning Forest (MSF) algorithm. Our goal is to improve this approach to the classification of hyperspectral images in urban areas. In the proposed method two spatial/texture features, using wavelet and Gabor filters, are first extracted. The Weighted Genetic (WG) algorithm is then used to obtain the subspace of hyperspectral data and texture features. They are then fed into a novel marker-based MSF classification algorithm. In this algorithm, the markers are extracted from the two spatial-spectral classification maps. To evaluate the efficiency of the proposed approach two image datasets, Pavia University acquired by ROSIS-03 and Berlin by HyMap, were used. Experimental results demonstrate that the proposed approach achieves approximately 17% and 14% better overall accuracy than the original MSF-based algorithm for these datasets, respectively.