Contextual Online Dictionary Learning for Hyperspectral Image Classification
Contextual Online Dictionary Learning for Hyperspectral Image Classification
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DOI：
10.1109/tgrs.2017.2761893
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发表时间：
2018-03
影响因子：
8.2
通讯作者：
Wei Fu;Shutao Li;Leyuan Fang;J. Benediktsson
中科院分区：
文献类型：
--
作者：
Wei Fu;Shutao Li;Leyuan Fang;J. Benediktsson
稀疏表示（SR）通过字典表示高光谱图像像素并产生判别稀疏系数，成功地应用于高光谱图像的分类。大多数基于sr的分类方法直接使用一些标记的像素作为原子来构建字典。这样的字典会导致大尺寸hsi的SR效率低下，并且当标记像素的数量小于光谱带的数量时可能是不完整的。本文提出了一种用于hsi分类的上下文在线字典学习（DL）方法，该方法在整个图像上学习字典，而不是在少数标记像素上学习字典。该方法通过在线学习机制有效地提高了不同像素点的自适应表示能力。具体来说，HSI的上下文特征与在线DL的判别谱信息相结合，即在邻域中推动相似的像素，以共享相对于学习良好的字典的相似稀疏系数。通过这种方法，得到的稀疏系数具有结构化和判别性。最后，将传统的分类器即线性支持向量机应用于稀疏系数，得到最终的分类结果。在实际hsi上的实验结果表明了该方法的有效性。
Sparse representation (SR) has been successfully used in the classification of hyperspectral images (HSIs) by representing HSI pixels over a dictionary and yielding discriminative sparse coefficients. Most of SR-based classification methods construct the dictionary by directly using some labeled pixels as atoms. Such dictionary can lead to inefficient SR for large-sized HSIs, and may be incomplete when the number of labeled pixels is less than the number of spectral bands. This paper proposes a contextual online dictionary learning (DL) method for HSIs classification, which learns a dictionary over the whole image rather than few labeled pixels. The proposed method can effectively and efficiently improve the adaptive representation capability of different pixels with an online learning mechanism. Specifically, the contextual characteristics of the HSI are integrated with discriminative spectral information for online DL, i.e., pushing similar pixels in neighborhood to share similar sparse coefficients with respect to the well-learned dictionary. By this way, the obtained sparse coefficients are structured and discriminative. Finally, a traditional classifier, i.e., the linear support vector machine, is applied to the sparse coefficients, and the final classification results are obtained. Experimental results on real HSIs show the effectiveness of the proposed method.