Hyperspectral-image classification method combining superpixel dimension reduction with post-processing optimization
Hyperspectral image(HSI)classification is one of the fundamental tasks in the field of applied remote sensing.As technological advances have increased the spatial and spectral resolutions available for data acquisition,the problem of achieving accurate HSI classification is becoming more challenging.This problem is especially true for the HSI data with small labeled training samples and insufficient utilization of spatial-spectral information in HSI classification models.Aiming at these problems,this paper proposes a new HSI classification method(expressed as SKERW_SVM)by combining the Superpixel Dimension Reduction(SDR)with post-processing optimization.First,we develop a Superpixel Sparse Linear Discriminant Analysis(SSLDA)method by combining Regional Clustering(RC)with SLDA.In the SSLDA method,the RC is applied to construct a homogeneous local neighborhood set with high spatial correlation and spectral similarity for each pixel of the HSI.The SLDA is used to extract superpixel sparse mixture features that can fully characterize spatial-spectral information and related change information of the HSI based on the constructed homogeneous regions.Then,the extracted sparse mixture features are inputted into the support vector machine to generate the class probabilities of all pixels.Finally,the original class probabilities are optimized in the post-processing step by the extended random walker that can express the spatial relationship among adjacent pixels quantitatively.The classification map is obtained according to the maximum probability.To assess the performance of the proposed method,a series of experiments is conducted on three small-scale HSI datasets,including Indian Pines,University of Pavia,and Salinas,as well as a large-scale HSI dataset HoustonU.The proposed SKERW_SVM obtains overall accuracies of 98.58％,96.88％,98.54％,and 91.01％on Indian Pines,University of Pavia,Salinas,and HoustonU,respectively.Experimental results demonstrate that our SKERW_SVM can fully mine the joint spatial-spectral features of HSI and achieve higher classification accuracy under the case of small labeled training samples compared with several related advanced methods.Moreover,the operation time consumed by SKERW_SVM is more appropriate than that by other methods.Under the lack of the labeled HSI pixel condition,the proposed HSI classification method by combining the SDR with post-processing optimization can efficiently extract the high-discrimination mixture feature information of HSI and significantly enhance classification performance.The SDR based on the homogeneous local regions,one of the components of the SKERW_SVM classification model,can greatly reduce the data redundancy and fully extract the information of spatial and spectral signatures compared with pixel-wise dimension-reduction methods.Meanwhile,the extended random walker in the post-processing step can fully use the spatial information of HSI by constructing a relationship graph to optimize the original class probabilities,thereby further improving the classification performance.
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