Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification
This work addresses domain adaptation for hyperspectral image classification, which is an incremental improvement for remote sensing applications.
The paper tackles hyperspectral image classification with limited labeled data by proposing a tensor alignment domain adaptation method that segments images into superpixels and projects tensors into an invariant subspace using Tucker decomposition with manifold regularization. Experimental results on four real datasets show it achieves better performance than state-of-the-art subspace learning methods.
This paper presents a tensor alignment (TA) based domain adaptation method for hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first segmented into superpixels and tensors of both domains are constructed to include neighboring samples from single superpixel. Then we consider the subspace invariance between two domains as projection matrices and original tensors are projected as core tensors with lower dimensions into the invariant tensor subspace by applying Tucker decomposition. To preserve geometric information in original tensors, we employ a manifold regularization term for core tensors into the decomposition progress. The projection matrices and core tensors are solved in an alternating optimization manner and the convergence of TA algorithm is analyzed. In addition, a post-processing strategy is defined via pure samples extraction for each superpixel to further improve classification performance. Experimental results on four real HSIs demonstrate that the proposed method can achieve better performance compared with the state-of-the-art subspace learning methods when a limited amount of source labeled samples are available.