Fast Hyperspectral Image Classification Combining Transformers and SimAM-Based CNNs

计算机科学 高光谱成像 模式识别(心理学) 人工智能 判别式 卷积神经网络 特征提取 像素 水准点(测量) 上下文图像分类 图像(数学) 大地测量学 地理
作者
Lianhui Liang,Ying Zhang,Shaoquan Zhang,Jun Li,Antonio Plaza,Xudong Kang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-19 被引量:30
标识
DOI:10.1109/tgrs.2023.3309245
摘要

Convolutional neural networks (CNNs) have been widely employed for hyperspectral image (HSI) classification due to their powerful ability to extract local spatial features. However, CNN-based methods cannot establish long-range dependencies among sequences of pixels. Transformers offer significant advantages when processing sequential data and can establish global relationships, but they still encounter a number of challenges, such as their limited spatial feature extraction ability, or their high computational cost. In order to address the aforementioned issues, we develop a new fast HSI classification approach combining transformers and SimAM-based CNNs. The latter are utilized to extract better spatial features, where the complex spatial characteristics of HSIs are retrieved using an improved hierarchical 2D dense network structure. A dual attention unit (DAU) mechanism is then utilized to direct the model's attention to discriminative spatial pixel characteristics and effective feature map channels, while suppressing information that is irrelevant for classification purposes. Regarding the spectral features, after extracting hierarchical local characteristics from various convolutional layers (using the hierarchical dense network structure), a squeezed-enhanced axial transformer is employed to establish global long-range dependencies whilst enhancing the ability of the model to extract local detail features in the HSI. Besides, a new Lion optimizer is utilized to improve the classification performance of our model. Our quantitative and comparative experiments on four benchmark datasets demonstrate the effectiveness of the proposed approach provides better classification results than other state-of-the-art approaches. Moreover, our FTSCN also achieves better classification results than other methods in practical scenarios.
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