Unsupervised Spatial–Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification

Mei, Shaohui; Ji, Jingyu; Geng, Yunhao; Zhi, Zhang; Xu, Li; Du, Qian

doi:10.1109/tgrs.2019.2908756

Cited by 243 publications

(96 citation statements)

References 49 publications

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“…Due to the fact that only a small number of pixels with labeled information can be used for training in HSIC. The training phase is different from the one of semantic segmentation model used in computer vision [34][35][36]. In training phase, the image containing training samples is used as the input, and the output is the predicted labels for all corresponding pixels.…”

Section: Patch-based Classification and Image-based Classification Fomentioning

confidence: 99%

“…More recently, various convolution neural networks with multiscale spatial-spectral features have been introduced for hyperspectral image classification [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. Jiao et al [28] used a pooling operation to generate multiple images from HSI, and a pretrained VGG-16 was introduced to extract multiscale features.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [35] proposed 3-D lightweight CNN for limited training samples and also presented two transfer learning strategies: (1) cross-sensor strategy and (2) cross-modal strategy. An unsupervised spatial-spectral feature learning strategy was proposed using 3-D CNN autoencoder to learn effective spatial-spectral features [36]. A multiscale deep middle-level feature fusion network was proposed to consider the complementary and related information among different scale features [37].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification.

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Section: Patch-based Classification and Image-based Classification Fomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Autoencoder is a common unsupervised framework and has been widely used for HSI [31]- [33]. Meanwhile, some improved methods of autoencoder are used in HSI classification, e.g., deep residual conv-deconv network [34] and 3-D convolutional autoencoder [35]. To further improve the classification accuracy, a self-taught learning framework This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: Introductionmentioning

confidence: 99%

Deep Multiview Learning for Hyperspectral Image Classification

Liu

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…As an effective solution, feature learning overcomes these issues and guarantees good classification accuracy [7][8][9][10][11][12][13]. The conventional feature learning methods, such as Principal Component Analysis (PCA) [9] and its variants [14][15][16] are widely applied in HSI.…”

Section: Introductionmentioning

confidence: 99%

Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Fang

Cai

et al. 2020

Sensors

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Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

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Unsupervised Spatial–Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification

Cited by 243 publications

References 49 publications

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

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

Deep Multiview Learning for Hyperspectral Image Classification

Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

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