Hyperspectral image classification based on spectral spatial feature extraction and deep rotation forest ensemble with AdaBoost

Lindiao Deng; Guo Cao; Ling Xu; Hao Xu; Qikun Pan; Lanwei Ding; Yanfeng Shang

doi:10.1117/12.2680064

27 June 2023 Hyperspectral image classification based on spectral spatial feature extraction and deep rotation forest ensemble with AdaBoost

Lindiao Deng, Guo Cao, Ling Xu, Hao Xu, Qikun Pan, Lanwei Ding, Yanfeng Shang

Author Affiliations +

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270519 (2023) https://doi.org/10.1117/12.2680064
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

In recent years, deep learning methods have been widely applied to hyperspectral image classification. However, these deep learning methods need lots of training samples to tune abundant parameters which induce a heavy computation burden. In this paper, we propose a classification model based on spectral spatial feature extraction and deep rotation forest ensemble with AdaBoost (SSDRA). First, linear discriminant analysis (LDA) and extended morphological attribute profile (EMAP) are used to extract features from hyperspectral images. In this way, the useful features of hyperspectral images can be integrated to a great extent while reducing the dimension of hyperspectral images. Then, the features of joint regions combining patches and superpixels are input into the classification model for training. Next, a deep rotation forest ensemble with AdaBoost (DRA) is designed for classification, so that our method can achieve superior performance with a small number of training samples. Finally, to optimize the classification results, superpixel smoothing is performed. The final results are obtained by using majority voting on the classification results within superpixels and among superpixels of different scales. To verify the effectiveness of the proposed method, experiments are performed using two public hyperspectral datasets. The experimental results demonstrate that the proposed method achieves satisfactory classification results.

Citation Download Citation

Lindiao Deng, Guo Cao, Ling Xu, Hao Xu, Qikun Pan, Lanwei Ding, and Yanfeng Shang "Hyperspectral image classification based on spectral spatial feature extraction and deep rotation forest ensemble with AdaBoost", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270519 (27 June 2023); https://doi.org/10.1117/12.2680064

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KEYWORDS

Feature extraction

Deep learning

Hyperspectral imaging

Random forests

Image classification

Image segmentation

Principal component analysis

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