Deep Convolutional Neural Network (CNN) shows excellent effectiveness on hyperspectral image (HSI) classification. However, the architecture design of CNN requires abundant expert knowledge and experience, which poses great prohibition to its wide application in real-world engineering. To alleviate the issue, this paper proposes an evolving block-based CNN (EB-CNN) to search the optimal architecture based on the genetic algorithm automatically. Specifically, two kinds of basic blocks with totally six different configurations are first designed to construct the search space. Then, a flexible encoding strategy is devised for the genetic algorithm to allow different chromosomes to evolve with different lengths. In this manner, the width of each layer and the depth of the architecture can be simultaneously optimized. Furthermore, a novel swapping mutation operator is proposed for the genetic algorithm to speed up the search efficiency and save computing resources. With the above techniques, the proposed algorithm automatically seeks the optimal CNN architecture for HSI classification, leading to its better usability than handcrafted CNNs. At last, extensive experiments conducted on 5 commonly used HSI datasets demonstrate that the proposed EB-CNN achieves highly competitive or even better performance, as compared to state-of-the-art peer algorithms.

Evolving Block-Based Convolutional Neural Network for Hyperspectral Image Classification

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

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Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images

Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing. However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images. Meanwhile, high-dimensional information processing also consumes significant time and computing power, or the extracted features may not be representative, resulting in unsatisfactory classification efficiency and accuracy. To solve these problems, an attention mechanism and depthwise separable convolution are introduced to the three-dimensional convolutional neural network (3DCNN). Thus, 3DCNN-AM and 3DCNN-AM-DSC are proposed for HRSI classification. Firstly, three hyperspectral datasets (Indian pines, University of Pavia and University of Houston) are used to analyze the patchsize and dataset allocation ratio (Training set: Validation set: Test Set) in the performance of 3DCNN and 3DCNN-AM. Secondly, in order to improve work efficiency, principal component analysis (PCA) and autoencoder (AE) dimension reduction methods are applied to reduce data dimensionality, and maximize the classification accuracy of the 3DCNN, but it will still take time. Furthermore, the HRSI classification model 3DCNN-AM and 3DCNN-AM-DSC are applied to classify with the three classic HRSI datasets. Lastly, the classification accuracy index and time consumption are evaluated. The results indicate that 3DCNN-AM could improve classification accuracy and reduce computing time with the dimension reduction dataset, and the 3DCNN-AM-DSC model can reduce the training time by a maximum of 91.77% without greatly reducing the classification accuracy. The results of the three classic hyperspectral datasets illustrate that 3DCNN-AM-DSC can improve the classification performance and reduce the time required for model training. It may be a new way to tackle hyperspectral datasets in HRSl classification tasks without dimensionality reduction.

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Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification

ABSTRACT In recent years, the wide use of deep learning based methods has greatly improved the classification performance of hyperspectral image (HSI). As an effective method to improve the performance of deep convolution networks, attention mechanism is also widely used for HSI classification tasks. However, the majority of the existing attention mechanisms for HSI classification are based on the convolution layer, and the classification accuracy still has margins for improvement. Motivated by the latest self attention mechanism in natural language processing, a deep transformer is proposed for HSI classification in this paper. Specifically, deep transformer along the spectral dimension and the spatial dimension are explored respectively. Then, a deep spatial-spectral transformer (DSS-TRM) is proposed to improve the classification performance of HSI. The contribution of this paper is to make full use of self attention mechanism, that is to use transformer layer instead of convolution layer. More importantly, a DSS-TRM is proposed to realize end-to-end HSI classification. Extensive experiments are conducted on three HSI data sets. The experimental results demonstrates that the proposed DSS-TRM could outperform the traditional convolutional neural networks and attention based methods.

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2021.2023910?needAccess=true

DSS-TRM: deep spatial–spectral transformer for hyperspectral image classification

Deep learning models have shown excellent performance in the hyperspectral remote sensing image (HSI) classification. In particular, convolutional neural networks (CNNs) have received widespread attention because of their powerful feature-extraction ability. Recently, a capsule network (CapsNet) was introduced to boost the performance of CNNs, marking a remarkable progress in the field of HSI classification. In this article, we propose a novel deep convolutional capsule neural network (DC-CapsNet) based on spectral–spatial features to improve the performance of CapsNet in the HSI classification while significantly reducing the computation cost of the model. Specifically, a convolutional capsule layer based on the extension of dynamic routing using 3-D convolution is used to reduce the number of parameters and enhance the robustness of the learned spectral–spatial features. Furthermore, a lighter and stronger decoder network composed of deconvolutional layers as a better regularization term and capable of acquiring more spatial relationships is used to further improve the HSI classification accuracy with low computation cost. In this study, we tested the performance of the proposed model on four widely used HSI datasets: the Kennedy Space Center, Indian Pines, Pavia University, and Salinas datasets. We found that the DC-CapsNet achieved high classification accuracy with limited training samples and effectively reduced the computation cost.

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Hyperspectral Remote Sensing Image Classification Using Deep Convolutional Capsule Network

With the rapid development of aerospace and various remote sensing platforms, the amount of data related to remote sensing is increasing rapidly. To meet the application requirements of remote sensing big data, an increasing number of scholars are combining deep learning with remote sensing data. In recent years, based on the rapid development of deep learning methods, research in the field of hyperspectral image (HSI) classification has seen continuous breakthroughs. In order to fully extract the characteristics of HSIs and improve the accuracy of image classification, this article proposes a novel three-dimensional (3-D) channel and spatial attention-based multiscale spatial–spectral residual network (termed CSMS-SSRN). The CSMS-SSRN framework uses a three-layer parallel residual network structure by using different 3-D convolutional kernels to continuously learn spectral and spatial features from their respective residual blocks. Then, the extracted depth multiscale features are stacked and input into the 3-D attention module to enhance the expressiveness of the image features from the two aspects of channel and spatial domains, thereby improving the accuracy of classification. The CSMS-SSRN framework proposed in this article can achieve better classification performance on different HSI datasets.

/pdf/3-d-channel-and-spatial-attention-based-multiscale-spatial-3blturv15e.pdf

Bin Xu

Papers

3-D Channel and Spatial Attention Based Multiscale Spatial–Spectral Residual Network for Hyperspectral Image Classification