POLSAR Image Classification via Clustering-WAE Classification Model

• Published in: IEEE access Vol. 6; pp. 40041 - 40049
• Main Authors: Xie, Wen, Xie, Ziwei, Zhao, Feng, Ren, Bo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AE network; Algorithms; Classification; Cluster analysis; Clustering; Clustering algorithms; clustering-WAE classification mode; Coders; Decoding; Image classification; K-means clustering algorithm; Linear programming; Matrix converters; Optimization; POLSAR image classification; Probability distribution; Radar imaging; Radar polarimetry; Statistical distributions; Synthetic aperture radar; Vector quantization; WAE classification model
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2852768

Considering the clustering algorithms could explore the label information automatically, this paper proposes a new method in terms of polarimetric synthetic aperture radar (POLSAR) image classification, which named a clustering-wishart-auto-encoder (WAE) classification model. With considering the statistical distribution characteristic of the POLSAR image, the WAE classification model, which proposed by ourselves, could improve the classification performance of the POLSAR image to some extent. The clustering-WAE classification model, that embedded the K-means clustering algorithm into the objective function of the WAE model, has the ability to improve the network performance. Our proposed method could minimize the difference of intra-class data and maximize the difference of inter-class data, from which the obtained POLSAR image features will be more compact to their corresponding cluster centers. Via simultaneously considering the compactness and statistical distribution of data, our method is capable of improving the POLSAR image classification results. The effectiveness of our proposed classification model has been demonstrated on four real POLSAR data sets.