- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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High Efficient Deep Feature Extraction and Classification of Spectral-Spatial Hyperspectral Image Using Cross Domain Convolutional Neural Networks
摘要： Recently, numerous remote sensing applications highly depend on the hyperspectral image (HSI). HSI classification, as a fundamental issue, has attracted increasing attention and become a hot topic in the remote sensing community. We implemented a regularized convolutional neural network (CNN), which adopted dropout and regularization strategies to address the overfitting problem of limited training samples. Although many kinds of the literature have confirmed that it is an effective way for HSI classification to integrate spectrum with spatial context, the scaling issue is not fully exploited. In this paper, we propose a high efficient deep feature extraction and the classification method for the spectral-spatial HSI, which can make full use of multiscale spatial feature obtained by guided filter. The proposed approach is the first attempt to lean a CNN for spectral and multiscale spatial features. Compared to its counterparts, experimental results show that the proposed method can achieve 3% improvement in accuracy, according to various datasets such as Indian Pines, Pavia University, and Salinas.
关键词： Convolutional neural network (CNN),hyperspectral image (HSI) classification,guided filter,spectral-spatial fusion
更新于2025-09-23 15:22:29
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11259 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part IV) || Integrating Convolutional Neural Network and Gated Recurrent Unit for Hyperspectral Image Spectral-Spatial Classification
摘要： In this paper, we propose a novel deep learning framework for hyperspectral image (HSI) spectral-spatial classification. This framework mainly consists of two components: convolutional neural network (CNN) and gated recurrent unit (GRU). CNN is used to automatically extract the high-level spatial features of each band, which are then fed into a fusion network based on GRUs. This fusion network combines feature-level fusion and decision-level fusion together in an end-to-end manner, thus sufficiently fusing the complementary information from different spectral bands. To demonstrate the effectiveness of the proposed method, we compare it with several state-of-the-art deep learning methods on two real HSIs. Experimental results show that the proposed method can achieve better performance than comparison methods.
关键词： Gated recurrent unit,Spectral-spatial fusion,Hyperspectral image classification,Convolutional neural network
更新于2025-09-10 09:29:36