Effective band selection of hyperspectral image by an attention mechanism-based convolutional network
Zengwei Zheng, Yi Liu, Mengzhu He, Dan Chen, Lin Sun, Fengle Zhu
Abstract
values and classification accuracies not only than the full-spectrum data, but also than the comparative band selection methods, including traditional SPA (successive projections algorithm) and GA (genetic algorithm) methods and the latest 2B-CNN algorithm. In addition, different from the traditional methods, the proposed band selection algorithm can effectively select bands while carrying out model training and can simultaneously take advantage of the original spectral-spatial information. The results confirmed the usefulness of the proposed attention mechanism-based convolutional network for selecting the most effective band combination of hyperspectral images.
Topics & Concepts
Hyperspectral imagingComputer scienceArtificial intelligenceSelection (genetic algorithm)Mechanism (biology)Pattern recognition (psychology)Image (mathematics)Convolutional neural networkPhysicsQuantum mechanicsRemote-Sensing Image ClassificationRemote Sensing and Land UseAdvanced Image Fusion Techniques