Mixed convolutional classification method for hyperspectral images based on spatial spectrum orthogonal constraints and bidirectional attention mechanism.

ABSTRACT: When classifying hyperspectral images, 3D-CNN and 2D-CNN are limited in their application due to excessive consumption of computing resources and difficulty in effectively extracting image features. Although the emergence of mixed convolution can reduce some limitations to a certain extent, there are also some issues. (1) After performing mixed convolution on images, there may be phenomena of information disappearance or redundancy. (2) In the case of small samples, classification accuracy will significantly decrease. (3) Maximum pooling is usually used in feature compression processing, but it ignores the relationships between features. To address these issues, this paper proposes a parallel convolution architecture and introduces spatial spectral orthogonal constraints and bidirectional

SUBMITTER: Wu T 

PROVIDER: S-EPMC12569078 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature