Spectral-Spatial Active Learning With Structure Density for Hyperspectral Classification

In this paper, a spectral-spatial active learning (AL) method is proposed based on an up-to-date unlabeled samples sampling strategy concentrated on the structure density supported by breaking ties. The proposed sampling criterion in AL is used for hyperspectral image classification, which involves several steps: First, superpixel segmentation algorithm is conducted on the HSI to cluster pixels with similar spectral-spatial signature into the same superpixel block. Then, density peak clustering technique is performed on the each superpixel block to obtain structure density of the pixels. Meanwhile, probability-based classifier is employed to achieve the probability distributions of pixel. Next, breaking ties (BT) score of each pixel can be calculated by exploiting the probabilities. Additionally, a fusion mechanism is introduced to select the unlabeled samples with representativeness and informativeness advantages by employing the BT-assisted structure density (SD sampling criterion) of each pixel. Finally, the samples with manual labeled class labels are put into the training set to retrain the classifier. Experimental results manifest that the proposed SD-based sampling criterion in active learning can significantly improve the classification accuracy in few labor costs. Thus, it has certain feasibility in practical application.