Content-Guided Convolutional Neural Network for Hyperspectral Image Classification

Liu, Qichao; Xiao, Liang; Yang, Jingxiang; Chan, Jonathan Cheung-Wai

doi:10.1109/tgrs.2020.2974134

Cited by 42 publications

(17 citation statements)

References 44 publications

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“…As more training samples of hyperspectral remote sensing images are provided, the test accuracy can be improved, and it will finally stop increasing or increase slowly [29,40]. Therefore, there is a trade-off between the ratio of training samples and the test performance of hyperspectral remote sensing images.…”

Section: The Effects Of Different Ratio Of Training Samplesmentioning

confidence: 99%

“…A deep fully convolutional network (FCN) with an efficient nonlocal module (ENL-FCN) was proposed by Shen et al [28] for HSI classification. A content-guided CNN (CGCNN) proposed by Liu et al [29] adjusts the kernel shape adaptively according to the spatial distribution of land covers. Tang et al [30] proposed a 3-D octave convolution with the spatial-spectral attention network (3DOC-SSAN) to capture discriminative spatial-spectral features.…”

Section: Introductionmentioning

confidence: 99%

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Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification

Ersoy

Fang

et al. 2021

Remote Sensing

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Recently, deep learning methods, for example, convolutional neural networks (CNNs), have achieved high performance in hyperspectral image (HSI) classification. The limited training samples of HSI images make it hard to use deep learning methods with many layers and a large number of convolutional kernels as in large scale imagery tasks, and CNN-based methods usually need long training time. In this paper, we present a wide sliding window and subsampling network (WSWS Net) for HSI classification. It is based on layers of transform kernels with sliding windows and subsampling (WSWS). It can be extended in the wide direction to learn both spatial and spectral features more efficiently. The learned features are subsampled to reduce computational loads and to reduce memorization. Thus, layers of WSWS can learn higher level spatial and spectral features efficiently, and the proposed network can be trained easily by only computing linear weights with least squares. The experimental results show that the WSWS Net achieves excellent performance with different hyperspectral remotes sensing datasets compared with other shallow and deep learning methods. The effects of ratio of training samples, the sizes of image patches, and the visualization of features in WSWS layers are presented.

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Section: The Effects Of Different Ratio Of Training Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification

Ersoy

Fang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Luo et al [24] proposed a hybrid-graph learning method to reveal the complex high-order relationships of the hyperspectral image. Liu et al [25] proposed a content-guided onvolutional neural network. Samat et al [ 26] proposed an edge gradient-based active learning approach.…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Image Classification Method Using Multifeature Vectors and Optimized KELM

Chen

Miao

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

125

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To improve the accuracy and generalization ability of hyperspectral image classification, a feature extraction method integrating principal component analysis(PCA) and local binary pattern (LBP) is developed for hyperspectral images in this paper. The PCA is employed to reduce the dimension of the spectral features of hyperspectral images. The LBP with low computational complexity is used to extract the local spatial texture features of hyperspectral images to construct multi-feature vectors. Then the gray wolf optimization(GWO) algorithm with global search capability is employed to optimize the parameters of kernel extreme learning machine(KELM) to construct an optimized KELM model, which is used to effectively realize a hyperspectral image classification (PLG-KELM) method. Finally, the Indian pines dataset, Houston dataset and Pavia University dataset and an application of WHU-Hi-LongKou dataset are selected to verify the effectiveness of the PLG-KELM. The comparison experiment results show that the PLG-KELM can obtain higher classification accuracy, and takes on better generalization ability for small samples. It provides a new idea for processing hyperspectral images.

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“…Cao et al [15] proposed a unified Bayesian framework in which a CNN coupled with Markov random fields are utilized to classify HSI. Liu et al [16] proposed a content-guided CNN to reduce the misclassification of pixels, particularly those near the cross-classes regions. Jia et al [17] proposed a 3D Gabor CNN in which CNN kernels are replaced with 3D Gabor-modulated kernels, to improve the robustness against the scale and orientation changes.…”

Section: Introductionmentioning

confidence: 99%

SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification

2021

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Convolutional neural networks (CNNs) are the go-to model for hyperspectral image (HSI) classification because of the excellent locally contextual modeling ability that is beneficial to spatial and spectral feature extraction. However, CNNs with a limited receptive field pose challenges for modeling long-range dependencies. To solve this issue, we introduce a novel classification framework which regards the input HSI as a sequence data and is constructed exclusively with multilayer perceptrons (MLPs). Specifically, we propose a spectral-spatial MLP (SS-MLP) architecture, which uses matrix transposition and MLPs to achieve both spectral and spatial perception in global receptive field, capturing long-range dependencies and extracting more discriminative spectral-spatial features. Four benchmark HSI datasets are used to evaluate the classification performance of the proposed SS-MLP. Experimental results show that our pure MLP-based architecture outperforms other state-of-the-art convolution-based models in terms of both classification performance and computational time. When comparing with the SSSERN model, the average accuracy improvement of our approach is as high as 3.03%. We believe that our impressive experimental results will foster additional research on simple yet effective MLP-based architecture for HSI classification.

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Content-Guided Convolutional Neural Network for Hyperspectral Image Classification

Cited by 42 publications

References 44 publications

Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification

Wide Sliding Window and Subsampling Network for Hyperspectral Image Classification

A Hyperspectral Image Classification Method Using Multifeature Vectors and Optimized KELM

SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification

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