Matrix-Based Discriminant Subspace Ensemble for Hyperspectral Image Spatial–Spectral Feature Fusion

Hang, Renlong; Liu, Qingshan; Song, Hwangjun; Sun, Yubao

doi:10.1109/tgrs.2015.2465899

Cited by 90 publications

(34 citation statements)

References 38 publications

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“…In addition to these, graph-based methods [5], extreme learning machine [6], sparse representation-based classifier [7], and many other methods have been further employed to promote the performance of hyperspectral image classification. Nevertheless, it is difficult to distinguish different land-cover categories accurately by only using the spectral information [8]. With the observation that spatially neighboring pixels usually carry correlated information within a smooth spatial domain, many researchers have resorted to spectral-spatial classification methods and several models have been proposed to exploit such local continuity [9], [10].…”

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confidence: 99%

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Wan

Gong

Zhong

et al. 2020

IEEE Trans. Geosci. Remote Sensing

346

141

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Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, so they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information. Different from the commonly used GCN models which work on a fixed graph, we enable the graph to be dynamically updated along with the graph convolution process, so that these two steps can be benefited from each other to gradually produce the discriminative embedded features as well as a refined graph. Moreover, to comprehensively deploy the multi-scale information inherited by hyperspectral images, we establish multiple input graphs with different neighborhood scales to extensively exploit the diversified spectral-spatial correlations at multiple scales. Therefore, our method is termed 'Multi-scale Dynamic Graph Convolutional Network' (MDGCN). The experimental results on three typical benchmark datasets firmly demonstrate the superiority of the proposed MDGCN to other state-of-the-art methods in both qualitative and quantitative aspects.

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confidence: 99%

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Wan

Gong

Zhong

et al. 2020

IEEE Trans. Geosci. Remote Sensing

346

141

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“…We compared the proposed Bi-CLSTM model with several FE methods, including regularized local discriminant embedding (RLDE) [50], matrix-based discriminant analysis (MDA) [3], 2D-CNN, 3D-CNN, LSTM [49], and CNN+LSTM. We train DL models on a single TITAN X GPU and implement them in TensorFlow.…”

Section: Methodsmentioning

confidence: 99%

“…For Indian Pines and KSC datasets, we randomly select 10% pixels from each class as the training set, and use the remaining pixels as the testing set. The same as the experiments in [3,49], we randomly choose 3921 pixels as the training set and the rest of pixels as the testing set for the Pavia University dataset. The detailed numbers of training and testing samples are listed from Tables 1-3.…”

Section: Datasetsmentioning

confidence: 99%

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Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

et al. 2017

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This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center). The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5% as compared to 3D-CNN.

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“…The final classification is achieved by using a support vector machine classifier. Many other spectral and spatial joint features [18][19][20][21][22], such as 3D wavelet [18], spatial and spectral kernel [19], matrix-based discriminant subspace analysis [20], etc. are used for classification.…”

Section: Introductionmentioning

confidence: 99%

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

et al. 2017

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Abstract:The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a novel algorithm for spatial-spectral feature extraction based on hypergraph embedding. Firstly, each HSI pixel is regarded as a vertex and the joint of extended morphological profiles (EMP) and spectral features is adopted as the feature associated with the vertex. A hypergraph is then constructed by the K-Nearest-Neighbor method, in which each pixel and its most K relevant pixels are linked as one hyperedge to represent the complex relationships between HSI pixels. Secondly, the hypergraph embedding model is designed to learn a low dimensional feature with the reservation of geometric structure of HSI. An adaptive hyperedge weight estimation scheme is also introduced to preserve the prominent hyperedges by the regularization constraint on the weight. Finally, the learned low-dimensional features are fed to the support vector machine (SVM) for classification. The experimental results on three benchmark hyperspectral databases are presented. They highlight the importance of spatial-spectral joint features embedding for the accurate classification of HSI data. The weight estimation is better for further improving the classification accuracy. These experimental results verify the proposed method.

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Matrix-Based Discriminant Subspace Ensemble for Hyperspectral Image Spatial–Spectral Feature Fusion

Cited by 90 publications

References 38 publications

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

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