Superpixel-guided Discriminative Low-rank Representation of Hyperspectral Images for Classification
This work addresses classification challenges in remote sensing for hyperspectral images, offering an incremental improvement by combining existing techniques with a novel low-rank model.
The paper tackles hyperspectral image classification by proposing SP-DLRR, which integrates superpixel segmentation and discriminative low-rank representation to enhance pixel discriminability, achieving significant superiority over state-of-the-art methods, especially with limited training data.
In this paper, we propose a novel classification scheme for the remotely sensed hyperspectral image (HSI), namely SP-DLRR, by comprehensively exploring its unique characteristics, including the local spatial information and low-rankness. SP-DLRR is mainly composed of two modules, i.e., the classification-guided superpixel segmentation and the discriminative low-rank representation, which are iteratively conducted. Specifically, by utilizing the local spatial information and incorporating the predictions from a typical classifier, the first module segments pixels of an input HSI (or its restoration generated by the second module) into superpixels. According to the resulting superpixels, the pixels of the input HSI are then grouped into clusters and fed into our novel discriminative low-rank representation model with an effective numerical solution. Such a model is capable of increasing the intra-class similarity by suppressing the spectral variations locally while promoting the inter-class discriminability globally, leading to a restored HSI with more discriminative pixels. Experimental results on three benchmark datasets demonstrate the significant superiority of SP-DLRR over state-of-the-art methods, especially for the case with an extremely limited number of training pixels.