Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

Fu, Peng; Sun, Xin; Sun, Quansen

doi:10.3390/rs9121237

Cited by 19 publications

(7 citation statements)

References 34 publications

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“…Moreover, the network can greatly reduce the complexity of the model without affecting the classification accuracy. Fu et al proposed a super-pixel segmentation algorithm [47]. For high-texture hyperspectral images, the algorithm can decompose them into inhomogeneous blocks, which well maintains the homogeneous characteristic.…”

Section: Introductionmentioning

confidence: 99%

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

et al. 2021

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Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral–spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models.

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Section: Introductionmentioning

confidence: 99%

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

et al. 2021

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“…Many algorithms have been designed for hyperspectral applications such as classification, feature extraction, and segmentation, etc. [3][4][5][6][7][8][9][10][11][12] A LiDAR system uses the pulsed laser to measure distances, which belongs to the active remote sensing. For a LiDAR system, the coherent light pulses are transmitted, reflected by objects on the ground, and catched by a receiver.…”

Section: Introductionmentioning

confidence: 99%

Deep Residual Network-Based Fusion Framework for Hyperspectral and LiDAR Data

Sun

et al. 2021

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

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This paper presents a deep residual network-based fusion framework for hyperspectral and LiDAR data. In this framework, three new fusion methods are proposed, which are the residual network-based deep feature fusion (RNDFF), the residual network-based probability reconstruction fusion (RNPRF) and the residual network-based probability multiplication fusion (RNPMF). The three methods use extinction profile (EP), local binary pattern (LBP), and deep residual network. Specifically, EP and LBP features are extracted from two sources and stacked as spatial features. For RNDFF, the deep features of each source are extracted by a deep residual network, and then the deep features are stacked to create the fusion features which are classified by softmax classifier. For RNPRF, the deep features of each source are input to the softmax classifier to obtain the probability matrices, and then the probability matrices are fused by weighted addition to producing the final label assignment. For RNPMF, the probability matrices are fused by array multiplication. Experimental results demonstrate that the classification performance of the proposed methods significantly outperform existing methods in hyperspectral and LiDAR data fusion.

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“…Currently, various algorithms are only designed for HSI or LiDAR. Several algorithms for classification, feature extraction, and segmentation are proposed for HSI [2][3][4][5][6][7][8][9][10], while many feature extraction and detection algorithms are only designed for LiDAR [11][12][13][14][15][16]. However, it is evident that no single type of sensors can always be adequate for reliable image interpretation.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

Sun

et al. 2019

Remote Sensing

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A new method of superpixel segmentation-based local pixel neighborhood preserving embedding (SSLPNPE) is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), superpixel segmentation and local pixel neighborhood preserving embedding (LPNPE). A new workflow is proposed to calibrate the Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) data, which allows our method to be applied to actual data. Specifically, EP features are extracted from both sources. Then, the derived features of each source are fused by the SSLPNPE. Using the labeled samples, the final label assignment is produced by a classifier. For the open standard experimental data and the actual data, experimental results prove that the proposed method is fast and effective in hyperspectral and LiDAR data fusion.

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Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

Cited by 19 publications

References 34 publications

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

Deep Residual Network-Based Fusion Framework for Hyperspectral and LiDAR Data

Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

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