Contrastive MLP Network Based on Adjacent Coordinates for Cross-Domain Zero-Shot Hyperspectral Image Classification

With the breakthrough of transfer learning and meta-learning, cross-domain few-shot hyperspectral image classification (CDFSL HSIC) technology has recently achieved satisfactory performance under limited annotations. Nevertheless, the most practical applications are zero-shot scenarios, which are intractable for CDFSL technology, such as the extraterrestrial detection scene, where unexplored objects are recognized by scientists to be more valuable for research. To conquer the zero-shot problem under domain shift, a two-stage contrastive MLP network (MAC-CDZS) is proposed, which constitutes a pioneering effort in the cross-domain zero-shot (CDZS) HSIC task. Firstly, given the remarkable performance of MLPs within a diminutive model size and their enhanced capacity for extracting spatial-spectral features of HSIs, the MLP framework has been strategically chosen as the foundational backbone of the first stage in the MAC-CDZS for facilitating efficient feature extraction. Secondly, to alleviate the potential category collapse, the second-stage fine-tuning framework is introduced, which extends the first-stage backbone by incorporating the elaborate adjacent coordinate module and contrastive learning paradigm for more harmonious classification performance. Specifically, the adjacent coordinate module is creatively designed to adequately mine the adjacent coordinates among samples for ameliorating category collapse from the perspective of grasping more reliable priors. Furthermore, a contrastive learning paradigm is innovatively constructed, comprising a Spatial Augmentation (SA) module tailored for hyperspectral patches and a construction strategy of sample pair under zero-shot conditions, which aims to boost the representation capability and alleviate the class collapse. The superior performance of the MAC-CDZS is demonstrated by experimental results on four benchmark datasets.