Deep Multiscale Spectral-Spatial Feature Fusion for Hyperspectral Images Classification

Liang, Miaomiao; Jiao, Licheng; Yang, Shuyuan; Liu, Fang; Hou, Biao; Chen, Huan

doi:10.1109/jstars.2018.2836671

Cited by 57 publications

(30 citation statements)

References 48 publications

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“…Deep neural networks can automatically learn hierarchical feature representations from raw HSI data [42][43][44]. Compared with other deep networks, such as SAE [24], DBN [25], and the long short-term memory network (LSTM) [45], CNN can directly take 2D data as input, which provides a natural way to exploit the spatial information of HSIs.…”

Section: Hsi Classification Based On Cnnmentioning

confidence: 99%

Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

et al. 2019

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The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in HSI classification neglect adequately utilizing the strong complementary yet correlated information from each convolutional layer and only employ the last convolutional layer features for classification. In this paper, we propose a novel fully dense multiscale fusion network (FDMFN) that takes full advantage of the hierarchical features from all the convolutional layers for HSI classification. In the proposed network, shortcut connections are introduced between any two layers in a feed-forward manner, enabling features learned by each layer to be accessed by all subsequent layers. This fully dense connectivity pattern achieves comprehensive feature reuse and enforces discriminative feature learning. In addition, various spectral-spatial features with multiple scales from all convolutional layers are fused to extract more discriminative features for HSI classification. Experimental results on three widely used hyperspectral scenes demonstrate that the proposed FDMFN can achieve better classification performance in comparison with several state-of-the-art approaches.

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Section: Hsi Classification Based On Cnnmentioning

confidence: 99%

Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

et al. 2019

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“…However, transferring pre-trained (by natural images) deep network parameters still hard to keep the raw spectral features of hyperspectral images. Therefore, as we do in [32], one layer of collaborative sparse AE is added to the hidden layer of the proposed S-RAE network for fusing the spectral-spatial feature, which is abbreviated as S-RCAE and given by…”

Section: Multiscale Spectral-spatial Feature Fusionmentioning

confidence: 99%

“…The main innovation in this paper is S-RAE for more discriminative feature learning. In order to verify its effectiveness, we severally compare the classification accuracy of DSaF processed by S-RAE and SAE, as well as deep spectral-spatial fusion feature by S-RCAE and CAE in [32], respectively. All experiments here are conducted on three experimental datasets except Indian Pines.…”

Section: Stepwise Evaluation Of the Proposed Strategiesmentioning

confidence: 99%

“…Deep spatial feature (DSaF), which is extracted by the filter banks transferred from a pre-trained deep CNN, exhibits significant performance for HSIC, while fusing it with the raw spectral feature (SeF) further enhances its discrimination [20,32]. However, from the visualization in Figure 1, SeF shows obvious intra-class manifold but weak inter-class separability, while DSaF does just the reverse.…”

Section: Introductionmentioning

confidence: 99%

“…However, from the visualization in Figure 1, SeF shows obvious intra-class manifold but weak inter-class separability, while DSaF does just the reverse. Collaborative auto-encoder (CAE), as did in [32], greatly combines both of their advantages and shows excellent performance. Nevertheless, the excessive intra-class discretization in DSaF caused the fusion processing difficult to preserve the potential manifold in SeF.…”

Section: Introductionmentioning

confidence: 99%

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A Superpixel-Based Relational Auto-Encoder for Feature Extraction of Hyperspectral Images

2019

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Filter banks transferred from a pre-trained deep convolutional network exhibit significant performance in heightening the inter-class separability for hyperspectral image feature extraction, but weakening the intra-class consistency simultaneously. In this paper, we propose a new superpixel-based relational auto-encoder for cohesive spectral–spatial feature learning. Firstly, multiscale local spatial information and global semantic features of hyperspectral images are extracted by filter banks transferred from the pre-trained VGG-16. Meanwhile, we utilize superpixel segmentation to construct the low-dimensional manifold embedded in the spectral domain. Then, representational consistency constraint among each superpixel is added in the objective function of sparse auto-encoder, which iteratively assist and supervisedly learn hidden representation of deep spatial feature with greater cohesiveness. Superpixel-based local consistency constraint in this work not only reduces the computational complexity, but builds the neighborhood relationships adaptively. The final feature extraction is accomplished by collaborative encoder of spectral–spatial feature and weighting fusion of multiscale features. A large number of experimental results demonstrate that our proposed method achieves expected results in discriminant feature extraction and has certain advantages over some existing methods, especially on extremely limited sample conditions.

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Classification of Hyperspectral Images with Various Spatial Features

Shinde¹,

Patidar²

2021

Smart Computing Techniques and Applications

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Deep Multiscale Spectral-Spatial Feature Fusion for Hyperspectral Images Classification

Cited by 57 publications

References 48 publications

Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

A Superpixel-Based Relational Auto-Encoder for Feature Extraction of Hyperspectral Images

Classification of Hyperspectral Images with Various Spatial Features

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