Novel Class Discovery for Hyperspectral Image via Class-Relation Perceptive Distillation With Prototype-Level Clustering Prediction

Confronted with the increasing emergency of hyperspectral remote sensing categories in the dynamic environment, traditional classification models that depend on fixed-category labeled data encounter difficulties on new classes recognition. Novel class discovery (NCD) aims to discover unknown class-disjoint novel classes in an unlabeled dataset with the pre-existing knowledge of known classes. Notably, the critical goal of NCD is to ensure recognition accuracy of known classes while identifying new ones. In this paper, we propose a class-relation perceptive distillation with prototype-level clustering prediction network (CRPD-PCP) for NCD of hyperspectral image (HSI). The proposed framework comprises an initial training stage (ITS) and a novel class discovery stage (NCDS) with two essential modules. Specifically, we present the class relation perceptive distillation (CRPD) module, which imposes a similarity constraint on the prediction of the distribution of new class data over the models of two stages. With the CRPD operated on the NCDS, our model effectively captures class relation information in spectral-spatial domain between known and novel classes of HSI to avoid forgetting old knowledge. Besides, we establish the prototype-level clustering prediction (PCP) module to generate high-confidence pseudo-labels for unlabeled novel classes. To be specific, we progressively cluster samples with the same spectral angular distance from the perspective of prototypes, and the self-supervised prototype-level knowledge distillation strategy in PCP facilitates effective identification of new categories. Experiments conducted on four datasets demonstrate that the CRPD-PCP model generates superior performance compared to other NCD methods for HSI. Our code will be released at https://github.com/Chirsycy/CRPD-PCP.git.