Integration of 3-dimensional discrete wavelet transform and Markov random field for hyperspectral image classification

详细信息    查看全文

• 作者：Xiangyong Cao ^{caoxiangyong45@gmail.com}Author Vitae ; Lin Xu ^{xulinshadow@gmail.com}Author Vitae ; Deyu Meng ; ^{dymeng@mail.xjtu.edu.cn}Author Vitae ; Qian Zhao ^{timmy.zhaoqian@gmail.com}Author Vitae ; Zongben Xu ^{zbxu@mail.xjtu.edu.cn}Author Vitae
• 关键词：00-01 ; 99-00
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：22 February 2017
• 年：2017
• 卷：226
• 期：Complete
• 页码：90-100
• 全文大小：1721 K
• 卷排序：226

文摘

Hyperspectral image (HSI) classification is one of the fundamental tasks in HSI analysis. Recently, many approaches have been extensively studied to improve the classification performance, among which integrating the spatial information underlying HSIs is a simple yet effective way. However, most of the current approaches haven't fully exploited the spatial information prior. They usually consider this prior either in the step of extracting spatial feature before classification or in the step of post-processing label map after classification, while don't integratively employ the prior in both steps, which thus leaves a room for further enhancing their performance. In this paper, we propose a novel spectral-spatial HSI classification method, which fully utilizes the spatial information in both steps. Firstly, the spatial feature is extracted by applying the 3-dimensional discrete wavelet transform (3D-DWT). Secondly, the local spatial correlation of neighboring pixels is modeled using Markov random field (MRF) based on the probabilistic classification map obtained by applying probabilistic support vector machine (SVM) to the extracted 3D-DWT feature in the first step, and then a maximum a posterior (MAP) classification problem can be formulated in a Bayesian perspective. Finally, α-Expansion min-cut-based optimization algorithm is adopted to solve this MAP problem efficiently. Experimental results on two benchmark HSIs show that the proposed method achieves a significant performance gain beyond state-of-the-art methods.