Guided Filter of Random Patches Network and Relaxed-Collaborative-Representation-Based Hyperspectral Image Classification

Abstract

Feature extraction and accurate classification are crucial tasks in land-cover classification of hyperspectral image (HSI). We propose guided filter (GF) of random patches network (RPNet) and relaxed collaborative representation (RCR) based HSI classification (HSIC) method called GRR. The shallow and deep features are extracted using RPNet that requires no pretraining stage. In addition to the obtained feature set, the original HSI and extracted features are then filtered by GF to preserve the edge details. After that, all the distinct feature sets are separately concatenated with the original HSI to keep the original structure of the data. The high-dimensional feature sets are then processed by linear discriminant analysis (LDA) to increase class separability and to select the most representative features. Since few train samples are available in HSIC task, efficiency of LDA is improved using superpixel segmentation to generate pseudosamples. As final stage, the reduced-dimension feature sets are classified by the use of superpixel-guided RCR which utilize the resemblance and discrimination of the feature sets efficiently. The extensive experiments on the real HSIs are carried out to validate the efficacy of the proposed method.