Abstract
Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.
Highlights
Continuous development of hyperspectral sensors makes it easy to collect a large amount of hyperspectral data, labeling the acquired data is expensive [1]
This may be due to the fact that the spatial spectral information of this image can be more fully explored by using adaptive size and shape of superpixels in the classification
The classification accuracy of our method is improved by at least 2% (97.85% vs. 95.83%) contrast to other four superpixel-level competitors, superpixel-based classification via multiple kernels (SCMK), SSC-SL, multiscale dynamic graph convolutional network (MDGCN) and superpixel pooling convolutional neural network (SPCNN)
Summary
Continuous development of hyperspectral sensors makes it easy to collect a large amount of hyperspectral data, labeling the acquired data is expensive [1]. Unlike big data recorded in other fields, one of the remarkable features of hyperspectral data is that it clearly contains spatial structure information in addition to spectral information This means that classification results may be improved by integrating spatial and spectral information based on classification techniques or classifier. Using fixed-size window technique, the methods of Markov random field [17], guided filter [18], discontinuity preserving relaxation [19,20], and recursive filtering [21] successfully adopted spatial information to smooth the noisy pixels contained in the HSI. The use of these smoothing techniques in Remote Sens.
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