Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization
Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization
复制标题
DOI：
10.1109/tcyb.2015.2484324
复制
发表时间：
2016-12
影响因子：
11.8
通讯作者：
Yuan Yuan-Yuan;J. Lin;Qi Wang
中科院分区：
文献类型：
--
作者：
Yuan Yuan-Yuan;J. Lin;Qi Wang
高光谱图像分类是遥感领域的一个关键问题。准确的分类有利于土地利用分析和海洋资源利用等大量应用。但数据相关性高给分类带来了困难，特别是对于光谱信息丰富的恒生指数。此外，传统方法往往不能很好地考虑恒指的空间相干性，这也限制了分类性能。为了解决这些固有的障碍，本文提出了一种新的光谱空间分类方案。该方法主要关注多任务联合稀疏表示（MJSR）和逐步马尔可夫随机场框架，这被认为是该过程的两个主要贡献。首先，MJSR不仅减少了光谱冗余，而且在分类过程中保留了光谱场中必要的相关性。其次，逐步优化进一步挖掘空间相关性，显著提高分类精度和鲁棒性。在几个通用的质量评价指标方面，在印度松木和帕维亚大学的实验结果表明，我们的方法与最先进的竞争对手相比具有优势。
Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.