Spectrum-Induced Transformer-Based Feature Learning for Multiple Change Detection in Hyperspectral Images

The multiple change detection (MCD) of hyperspectral images (HSIs) is the process of detecting change areas and providing "from-to" change information of HSIs obtained from the same area at different times. HSIs have hundreds of spectral bands and contain a large amount of spectral information. However, current deep-learning-based MCD methods do not pay special attention to the interspectral dependency and the effective spectral bands of various land covers, which limits the improvement of HSIs' change detection (CD) performance. To address the above problems, we propose a spectrum-induced transformer-based feature learning (STFL) method for HSIs. The STFL method includes a spectrum-induced transformer-based feature extraction module (STFEM) and an attention-based detection module (ADM). First, the 3D-2D convolutional neural networks (CNNs) are used to extract deep features, and the transformer encoder (TE) is used to calculate self-attention matrices along the spectral dimension in STFEM. Then, the extracted deep features and the learned self-attention matrices are dot-multiplied to generate more discriminative features that take the long-range dependency of the spectrum into account. Finally, ADM mines the effective spectral bands of the difference features learned from STFEM by the attention block (AB) to explore the discrepancy of difference features and uses the softmax function to identify multiple changes. The proposed STFL method is validated on two hyperspectral datasets, and their experiments illustrate the superiority of the proposed STFL method over the currently existing MCD methods.