Low-rank representation for 3D hyperspectral images analysis from map perspective

Hyperspectral images naturally stand as 3D data, which carry semantic information in remote sending applications. To well utilize 3D hyperspectral images, signal processing and learning techniques have been widely exploited, and the basis is to divide a given hyperspectral data into a set of semantic classes for analysis, i.e.; segmentation. To segment given hyperspectral data is an important and challenging research theme. Recently, to reduce the amount of human labor required to label samples in hyperspectral image segmentation, many approaches have been proposed and achieved good performance with a few labeled samples. However, most of them fail to exploit the high spectral correlation in distinct bands and utilize the spatial information of hyperspectral data. In order to overcome these drawbacks, a novel framework jointing the maximum a posteriori (MAP) model and low-rank representation (LRR) is proposed. In this paper, low-rank representation, conducted as a latent variables, can exploit the high spectral correlation in distinct bands and obtain a more compact and discriminative representation. On the other hand, a novel MAP framework is driven by using low-rank representation coefficient as latent variables, which will improve the probability that the closer pixels can be divided into the same class. The experiment results and quantitative analysis demonstrate that the proposed approach is effective and can obtain high segmentation accuracy compared with state-of-the-art approaches.