Dropout Concrete Autoencoder for Band Selection on Hyperspectral Image Scenes

作者：Lei Xu, Mete Ahishali, Moncef Gabbouj · 发表于：IEEE Geoscience and Remote Sensing Letters · 年份：2025 · DOI：10.1109/lgrs.2025.3564478 · 被引用次数：4 · 研究领域：Remote-Sensing Image Classification

Deep learning-based informative band selection methods on hyperspectral images (HSI) have recently gained intense attention to eliminate spectral correlation and redundancies. However, existing deep learning-based methods either need additional post-processing strategies to select the descriptive bands or optimize the model indirectly due to the parameterization inability of discrete variables for the selection procedure. To overcome these limitations, this work proposes a novel end-to-end network for informative band selection. The proposed network, named Dropout CAE, is inspired by advances in the concrete autoencoder (CAE) and dropout feature ranking (Dropout FR) strategy. Unlike traditional deep learning-based methods; the Dropout CAE is trained directly given the required band subset, eliminating the need for further post-processing. Experimental results in four HSI scenes show that the Dropout CAE achieves substantial and effective performance levels that outperform competing methods. The code is available at https: //github.com/LeiXuAI/Hyperspectral.