Multiple Morphological Component Analysis Based Decomposition for Remote Sensing Image Classification

作者：Xu, Xiang; Li, Jun^*; Huang, Xin; Mura, Mauro Dalla; Plaza, Antonio

来源：IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5): 3083-3102.

DOI：10.1109/TGRS.2015.2511197

• Decomposition
• image separation
• multinomial logistic regression (MLR)
• multiple morphological component analysis (MMCA)
• sparse representation
• textural features

Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples.

• 出版日期2016-5
• 单位中山大学; 武汉大学; 电子科技大学