Exploring Oblique Rotation Factor to Restructure Deep Hyperspectral Image Classification

高光谱成像 人工智能 斜格 计算机科学 卷积神经网络 分类器(UML) 降维 模式识别(心理学) 旋转(数学) 数学 哲学 语言学
作者
Chunzhi Li,Xinyu Li,Jinbo Wang,Xiaohua Chen,Yuan Zhang
出处
期刊:IEEE Geoscience and Remote Sensing Letters [Institute of Electrical and Electronics Engineers]
卷期号:20: 1-5 被引量:2
标识
DOI:10.1109/lgrs.2023.3263296
摘要

Factor analysis (FA) is commonly used in fields such as economics and now being introduced as a new tool on dimensionality reduction (DR) for hyperspectral image classification (HSIC) , but FA usually employed orthogonal rotation to directly maximize the separation among factors, which would oversimplify the relationships between variables and factors, worse still, the orthogonal rotation often distorts the true relationships between underlying traits in real life and can not always accurately represent these relationships. To this end, this letter proposes a DR algorithm about FA based on oblique rotation Oblimax to improve HSIC. Firstly, the common factors are extracted from the hyperspectral data to form a factor loading matrix which will be obliquely rotated, then its factor score is estimated to obtain the eigen dimensions for the hyperspectral data, thus realizing DR. On the basis of the successful DR, a deep classifier is constructed, specially, a double-branch structure about 3 dimensional-convolutional neural networks (3D-CNN) with different sizes is restructured to extract multi-scale spatial-spectral features, and early fusion is performed on the features, then 2 dimensional-convolutional neural networks (2D-CNN) is restructured to reduce the computational complexity and learns more spatial features. Finally, the accuracy of the proposed algorithm on the datasets Indian Pines, Kennedy Space Center and Muufl Gulfport, respectively achieves 99.78%, 99.95% and 95.57%. It shows that the proposed algorithm in this letter has advantages in improving the classification accuracy and reducing the complexity of computation.
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