Hyperspectral Image Classification using Few Shot Domain Adaptation and Active Learning

Hyperspectral images (HSIs) are pivotal in remote sensing, providing rich spectral and spatial information for applications such as agriculture, environmental monitoring, and mineral exploration. Unlike traditional RGB images, HSIs capture data across hundreds of contiguous spectral bands, enabling precise material classification based on unique spectral signatures. However, the high dimensionality and the need for pixel-level labeling pose significant challenges, as manual annotation is labor-intensive and time-consuming. To address this, we leverage Domain Adaptation, a transfer learning technique that facilitates knowledge transfer from a well-labeled source domain to a sparsely labeled or unlabeled target domain. In this paper, we focus on few-shot Domain Adaptation for HSI classification, where only a limited number of labeled target samples are available. Our approach integrates Active Learning to iteratively fine-tune a classifier by selecting the most informative target samples, thus reducing domain shift and enhancing model performance. We utilize a framework that generates domain-invariant embeddings by aligning the spectral and spatial features of HSIs from the source and target domains. This method significantly reduces the need for manual labeling while maintaining high classification accuracy. Our experimental results demonstrate the effectiveness of combining few-shot Domain Adaptation with Active Learning, achieving a remarkable 98 % accuracy in hyperspectral image classification with limited labeled data.