Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis

In geospatial image analysis, domain shifts caused by differences between datasets often undermine the performance of deep learning models due to their limited generalization ability. This issue is particularly pronounced in hyperspectral imagery, given the high dimensionality of the per-pixel reflectance vectors and the complexity of the resulting deep learning models. We introduce a semisupervised domain adaptation technique that improves on the adversarial discriminative framework, incorporating a novel multiclass discriminator to address low discriminability and negative transfer issues from which current approaches suffer. Significantly, our method addresses mode collapse by incorporating limited labeled data from the target domain for targeted guidance during adaptation. In addition, we integrate an attention mechanism that focuses on challenging spatial regions for the target mode. We tested our approach on three unique hyperspectral remote sensing datasets to demonstrate its efficacy in diverse conditions (e.g., cloud shadows, atmospheric variability, and terrain). This strategy improves discrimination and reduces negative transfer in domain adaptation for geospatial image analysis.