ODPA-CNN: One Dimensional Parallel Atrous Convolution Neural Network for Band-Selective Hyperspectral Image Classification

Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information. These methods can increase the accuracy of classification, but do not only focus on the spectral information, which is a big advantage of HSI. In addition, the 1D-CNN, which learns only pure spectral information, has limitations because it uses adjacent spectral information. In this paper, we propose a One Dimensional Parellel Atrous Convolution Neural Network (ODPA-CNN) that learns not only adjacent spectral information for HSI classification, but also spectral information from a certain distance. It extracts features in parallel to account for bands of varying distances. The proposed method excludes spatial information such as the shape of an object and performs HSI classification only with spectral information about the material of the object. Atrous convolution is not a convolution of adjacent spectral information, but a convolution between spectral information separated by a certain distance. We compare the proposed model with various datasets to the other models. We also test with the data we have taken ourselves. Experimental results show a higher performance than some 3D-CNN models and other 1D-CNN methods. In addition, using datasets to which random space is applied, the vulnerabilities of 3D-CNN are identified, and the proposed model is shown to be robust to datasets with little spatial information.

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HYPERSPECTRAL IMAGE CLASSIFICATION USING RESIDUAL 2D AND 3D CONVOLUTIONAL NEURAL NETWORK JOINT ATTENTION MODEL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliv-m-3-2021-187-2021 ◽

2021 ◽

Vol XLIV-M-3-2021 ◽

pp. 187-193

Author(s):

Q. Yuan ◽

Y. Ang ◽

H. Z. M. Shafri

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Classification Accuracy ◽

Spatial Information ◽

Hyperspectral Image ◽

Spectral Features ◽

Hyperspectral Image Classification ◽

Spatial Features ◽

3D Cnn ◽

2D And 3D

Abstract. Hyperspectral image classification (HSIC) is a challenging task in remote sensing data analysis, which has been applied in many domains for better identification and inspection of the earth surface by extracting spectral and spatial information. The combination of abundant spectral features and accurate spatial information can improve classification accuracy. However, many traditional methods are based on handcrafted features, which brings difficulties for multi-classification tasks due to spectral intra-class heterogeneity and similarity of inter-class. The deep learning algorithm, especially the convolutional neural network (CNN), has been perceived promising feature extractor and classification for processing hyperspectral remote sensing images. Although 2D CNN can extract spatial features, the specific spectral properties are not used effectively. While 3D CNN has the capability for them, but the computational burden increases as stacking layers. To address these issues, we propose a novel HSIC framework based on the residual CNN network by integrating the advantage of 2D and 3D CNN. First, 3D convolutions focus on extracting spectral features with feature recalibration and refinement by channel attention mechanism. The 2D depth-wise separable convolution approach with different size kernels concentrates on obtaining multi-scale spatial features and reducing model parameters. Furthermore, the residual structure optimizes the back-propagation for network training. The results and analysis of extensive HSIC experiments show that the proposed residual 2D-3D CNN network can effectively extract spectral and spatial features and improve classification accuracy.

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Hyperspectral Image Classification Based on Sparse Superpixel Graph

Remote Sensing ◽

10.3390/rs13183592 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3592

Author(s):

Yifei Zhao ◽

Fengqin Yan

Keyword(s):

Classification Accuracy ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Spectral Information ◽

Hyperspectral Image Classification ◽

Computational Burden ◽

Large Size ◽

High Classification Accuracy ◽

Good Classification

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.

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Hyperspectral Image Classification Based on Spectral and Spatial Information Using Multi-Scale ResNet

Applied Sciences ◽

10.3390/app9224890 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4890 ◽

Cited By ~ 2

Author(s):

Zong-Yue Wang ◽

Qi-Ming Xia ◽

Jing-Wen Yan ◽

Shu-Qi Xuan ◽

Jin-He Su ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Classification Accuracy ◽

Spatial Information ◽

Hyperspectral Image ◽

Computational Cost ◽

Data Cube ◽

Hyperspectral Image Classification ◽

Fixed Size ◽

Multi Scale

Hyperspectral imaging (HSI) contains abundant spectrums as well as spatial information, providing a great basis for classification in the field of remote sensing. In this paper, to make full use of HSI information, we combined spectral and spatial information into a two-dimension image in a particular order by extracting a data cube and unfolding it. Prior to the step of combining, principle component analysis (PCA) is utilized to decrease the dimensions of HSI so as to reduce computational cost. Moreover, the classification block used during the experiment is a convolutional neural network (CNN). Instead of using traditionally fixed-size kernels in CNN, we leverage a multi-scale kernel in the first convolutional layer so that it can scale to the receptive field. To attain higher classification accuracy with deeper layers, residual blocks are also applied to the network. Extensive experiments on the datasets from Pavia University and Salinas demonstrate that the proposed method significantly improves the accuracy in HSI classification.

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Spectral-Spatial Attention Networks for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs11080963 ◽

2019 ◽

Vol 11 (8) ◽

pp. 963 ◽

Cited By ~ 25

Author(s):

Xiaoguang Mei ◽

Erting Pan ◽

Yong Ma ◽

Xiaobing Dai ◽

Jun Huang ◽

...

Keyword(s):

Neural Network ◽

Image Classification ◽

Spatial Attention ◽

Spatial Information ◽

Hyperspectral Image ◽

Spatial Dimension ◽

Hyperspectral Image Classification ◽

Attention Networks ◽

Land Covers

Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance.

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Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs12122033 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2033 ◽

Cited By ~ 1

Author(s):

Xiaofei Yang ◽

Xiaofeng Zhang ◽

Yunming Ye ◽

Raymond Y. K. Lau ◽

Shijian Lu ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Spatial Information ◽

Hyperspectral Image ◽

Feature Learning ◽

Hyperspectral Image Classification ◽

3 Dimensional ◽

Recent Success ◽

Hybrid Module

Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D/3D CNNs.

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Deep convolution neural network with automatic attribute profiles for hyperspectral image classification

Multimedia Tools and Applications ◽

10.1007/s11042-020-10169-0 ◽

2021 ◽

Author(s):

Arundhati Das ◽

Kaushal Bhardwaj ◽

Swarnajyoti Patra

Keyword(s):

Neural Network ◽

Image Classification ◽

Hyperspectral Image ◽

Convolution Neural Network ◽

Hyperspectral Image Classification ◽

Deep Convolution Neural Network

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Active Semi-Supervised Random Forest for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs11242974 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2974 ◽

Cited By ~ 3

Author(s):

Youqiang Zhang ◽

Guo Cao ◽

Xuesong Li ◽

Bisheng Wang ◽

Peng Fu

Keyword(s):

Random Forest ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Data Sets ◽

Great Success ◽

Full Potential ◽

Hyperspectral Image Classification ◽

Unified Framework ◽

Proposed Model

Random forest (RF) has obtained great success in hyperspectral image (HSI) classification. However, RF cannot leverage its full potential in the case of limited labeled samples. To address this issue, we propose a unified framework that embeds active learning (AL) and semi-supervised learning (SSL) into RF (ASSRF). Our aim is to utilize AL and SSL simultaneously to improve the performance of RF. The objective of the proposed method is to use a small number of manually labeled samples to train classifiers with relative high classification accuracy. To achieve this goal, a new query function is designed to query the most informative samples for manual labeling, and a new pseudolabeling strategy is introduced to select some samples for pseudolabeling. Compared with other AL- and SSL-based methods, the proposed method has several advantages. First, ASSRF utilizes the spatial information to construct a query function for AL, which can select more informative samples. Second, in addition to providing more labeled samples for SSL, the proposed pseudolabeling method avoids bias caused by AL-labeled samples. Finally, the proposed model retains the advantages of RF. To demonstrate the effectiveness of ASSRF, we conducted experiments on three real hyperspectral data sets. The experimental results have shown that our proposed method outperforms other state-of-the-art methods.

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