Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

Cui, Ximin; Zheng, Ke; Gao, Lianru; Zhang, Bing; Yang, Dong; Ren, Jinchang

doi:10.3390/rs11192220

Cited by 32 publications

(18 citation statements)

References 57 publications

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“…Chen et al [46] proposed a classification model based on the integration of deep learning model and random subspace-based ensemble learning, and used transfer learning strategy to speed up the learning stage. Cui et al [47] presented a multi-scale spatial-spectral CNN network (HyMSCN) to integrate multiple receiving field fusion features and multi-scale spatial features at different levels. The fused feature is developed by using lightweight blocks of multiple reception fields (MRFF), which contains various types of dilated convolutions.…”

Section: Hyperspectral Image Classification Methods Based On 2d-3d Cnnmentioning

confidence: 99%

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

Cao

et al. 2020

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

112

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The emergence of convolutional neural network (CNN) has greatly promoted the development of hyperspectral image (HSI) classification technology. However, the acquisition of HSI is difficult. Lack of training samples is the primary cause of low classification performance. Traditional CNN-based methods mainly use 2D CNN for feature extraction, which make interband correlations of HSIs underutilized. 3D CNN extracts the joint-spectral-spatial information representation, but it depends on a more complex model. Also, too deep or too shallow network cannot extract the image features well. To tackle these issues, we propose an HSI classification method based on 2D-3D CNN and multi-branch feature fusion. We first combine 2D CNN and 3D CNN to extract image features. Then, by means of the multi-branch neural network, three kinds of features from shallow to deep are extracted and fused in the spectral dimension. Finally, the fused features are passed into several fully connected layers and a softmax layer to obtain the classification results. In addition, our network model utilizes the state-of-the-art activation function Mish to further improve the classification performance. Our experimental results, conducted on four widely used HSI data sets, indicate that the proposed method achieves better performance than the existing alternatives.

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Section: Hyperspectral Image Classification Methods Based On 2d-3d Cnnmentioning

confidence: 99%

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

Cao

et al. 2020

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

112

View full text Add to dashboard Cite

show abstract

“…Henceforward, the deep learning technique has been increasingly favored by the scientific community attributing to its great power of abstracting representations to classify hyperspectral cubes into certain land cover categories [20][21][22][23]. As a consequence, Cui et al (2019) proposed a multiscale spatial-spectral convolutional neural network (CNN) to integrate multiple receptive fields fused features and multiscale spatial features at different levels [24]. Gao et al (2019) integrated t-distributed stochastic neighbor embedding with a CNN to capture the potential assembly features of HSIs [25].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification with Localized Graph Convolutional Filtering

Sun

et al. 2021

Remote Sensing

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The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

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“…An alternative is to use a CNN model pre-trained on a large-scale dataset, e.g., ImageNet, as a feature extractor. The pre-trained model can be further fine-tuned with a relatively small-scale dataset, e.g., derived from remote sensing images, via transfer learning [27][28][29][30]. For example, Rezaee et al [31] investigated the use of CNNs to derive deep features for wetland extraction from optical remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks

Tang

Wang

2020

Remote Sensing

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Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. Image segmentation was performed to obtain image objects, while a fine-tuned CNN model was used to extract deep image features. We conducted feature selection based on the Gini index to reduce the dimensionality of deep features, and the selected features were then used for classifying tea objects via a random forest. The proposed method was first applied to Google Earth images and then transferred to GF-2 satellite images. We compared the proposed classification with existing methods: Object-based classification using random forest, Mask R-CNN, and object-based CNN without fine-tuning. The results show the proposed method achieved a higher classification accuracy than other methods and produced smaller over- and under-classification geometric errors than Mask R-CNN in terms of shape integrity and boundary consistency. The proposed approach, trained using Google Earth images, achieved comparable results when transferring to the classification of tea objects from GF-2 images. We conclude that the proposed method is effective for mapping tea plantations using very high-resolution remote sensing images even with limited training samples and has huge potential for mapping tea plantations in large areas.

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Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

Cited by 32 publications

References 57 publications

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion

Hyperspectral Image Classification with Localized Graph Convolutional Filtering

Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks

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