Spatial-Spectral Network for Hyperspectral Image Classification: A 3-D CNN and Bi-LSTM Framework

Yin, Junru; Qi, Changsheng; Chen, Qiqiang; Qu, Jiantao

doi:10.3390/rs13122353

Cited by 29 publications

(14 citation statements)

References 56 publications

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“…As a deep regression neural network, LSTM can handle long-term relationships of memory sequence information [ 22 ]. Many remote sensing image classification studies use LSTM to extract spatial and spectral features from images [ 23 , 24 , 25 ]. In this study, LSTM was used to explore the contextual dependencies between different regional feature sequences.…”

Section: Methodsmentioning

confidence: 99%

Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images

Hong

Zhong

Pan

et al. 2022

Sensors

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The accurate and timely identification of the degree of building damage is critical for disaster emergency response and loss assessment. Although many methods have been proposed, most of them divide damaged buildings into two categories—intact and damaged—which is insufficient to meet practical needs. To address this issue, we present a novel convolutional neural network—namely, the earthquake building damage classification net (EBDC-Net)—for assessment of building damage based on post-disaster aerial images. The proposed network comprises two components: a feature extraction encoder module, and a damage classification module. The feature extraction encoder module is employed to extract semantic information on building damage and enhance the ability to distinguish between different damage levels, while the classification module improves accuracy by combining global and contextual features. The performance of EBDC-Net was evaluated using a public dataset, and a large-scale damage assessment was performed using a dataset of post-earthquake unmanned aerial vehicle (UAV) images. The results of the experiments indicate that this approach can accurately classify buildings with different damage levels. The overall classification accuracy was 94.44%, 85.53%, and 77.49% when the damage to the buildings was divided into two, three, and four categories, respectively.

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Section: Methodsmentioning

confidence: 99%

Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images

Hong

Zhong

Pan

et al. 2022

Sensors

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“…Yin et al [ 27 ] developed a spatial-spectral mixed network for HSI categorization. The network collects spatial-spectral information from HSI using three layers of 3-D convolution and one layer of 2-D convolution.…”

Section: Literature Reviewmentioning

confidence: 99%

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Reddy¹,

Harikiran²,

Enduri

et al. 2022

Computational Intelligence and Neuroscience

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The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy.

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“…In recent studies, Yin et al [54] combined 3D CNN and a band grouping-based bidirectional long short-term memory (Bi-LSTM) network for HSI classification. In the network, the extracted spectral features were regarded as a procedure of processing sequence data, and the Bi-LSTM network acted as the spectral feature extractor to fully use the relationships between spectral bands.…”

Section: Existing Deficiencies and Future Prospectsmentioning

confidence: 99%

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

Luo

et al. 2021

Remote Sensing

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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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Spatial-Spectral Network for Hyperspectral Image Classification: A 3-D CNN and Bi-LSTM Framework

Cited by 29 publications

References 56 publications

Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images

Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

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

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