Combined sparse and collaborative representation for hyperspectral target detection

Li, Wei; Du, Qian; Zhang, Bing

doi:10.1016/j.patcog.2015.05.024

Cited by 225 publications

(66 citation statements)

References 30 publications

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“…Sparse representation-based detection (SRD) has been recently proposed [90,91,92,93,94,95,96,97,98]. The basic idea is that a target pixel can be better represented by target atoms than by background atoms.…”

Section: Representation-based Target and Anomaly Detectionmentioning

confidence: 99%

“…Our recent work [93] introduced a combined sparse and collaborative representation-based (CSCR) algorithm for target detection. The basic idea in the CSCR is that a testing sample is sparsely represented by target atoms because it can include only one target; meanwhile, it is collaboratively represented by background atoms because multiple background atoms may appear in a single pixel area.…”

Section: Algorithm 3 Cscr Target Detection Algorithmmentioning

confidence: 99%

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A survey on representation-based classification and detection in hyperspectral remote sensing imagery

2016

Pattern Recognition Letters

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115

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Section: Representation-based Target and Anomaly Detectionmentioning

confidence: 99%

Section: Algorithm 3 Cscr Target Detection Algorithmmentioning

confidence: 99%

A survey on representation-based classification and detection in hyperspectral remote sensing imagery

2016

Pattern Recognition Letters

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115

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“…Because the wavelength interval between every two neighboring bands is quite small (usually 10 nm), HSIs generally have a very high spectral resolution [1]. Analysis of HSIs has been widely used in a large variety of fields, including materials analysis, precision agriculture, environmental monitoring and surveillance [2][3][4]. Among the hyperspectral community, the HSIs classification is most vibrant filed of research which is to assign a unique class to each pixel in the image [5].…”

Section: Introductionmentioning

confidence: 99%

CSA-MSO3DCNN: Multiscale Octave 3D CNN with Channel and Spatial Attention for Hyperspectral Image Classification

Xiao

Wang

et al. 2020

Remote Sensing

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3D convolutional neural networks (CNNs) have been demonstrated to be a powerful tool in hyperspectral images (HSIs) classification. However, using the conventional 3D CNNs to extract the spectral–spatial feature for HSIs results in too many parameters as HSIs have plenty of spatial redundancy. To address this issue, in this paper, we first design multiscale convolution to extract the contextual feature of different scales for HSIs and then propose to employ the octave 3D CNN which factorizes the mixed feature maps by their frequency to replace the normal 3D CNN in order to reduce the spatial redundancy and enlarge the receptive field. To further explore the discriminative features, a channel attention module and a spatial attention module are adopted to optimize the feature maps and improve the classification performance. The experiments on four hyperspectral image data sets demonstrate that the proposed method outperforms other state-of-the-art deep learning methods.

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“…Hyperspectral imaging has opened up new opportunities for analyzing a variety of materials in remote sensing as it provides rich information on spectral and spatial dis-tributions of distinct materials. One of its most important applications is pixel classification, which is widely applied in material recognition, target detection, geoindexing, and so on [23,19,11,24]. However, the classification of hyperspectral image (HSI) still faces some challenges such as, the unbalance between a small number of available training samples and a large number of narrow spectral bands, the high variations of the spectral signature from identical material, high similarities of spectral signatures between some different materials, and the noise impact from the sensors and environment [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation

Chen

Zhou

et al. 2019

IEEE Access

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Deep learning has been widely used for hyperspectral pixel classification due to its ability of generating deep feature representation. However, how to construct an efficient and powerful network suitable for hyperspectral data is still under exploration. In this paper, a novel neural network model is designed for taking full advantage of the spectralspatial structure of hyperspectral data. Firstly, we extract pixel-based intrinsic features from rich yet redundant spectral bands by a subnetwork with supervised pre-training scheme. Secondly, in order to utilize the local spatial correlation among pixels, we share the previous subnetwork as a spectral feature extractor for each pixel in a patch of image, after which the spectral features of all pixels in a patch are combined and feeded into the subsequent classification subnetwork. Finally, the whole network is further fine-tuned to improve its classification performance. Specially, the spectral-spatial factorization scheme is applied in our model architecture, making the network size and the number of parameters great less than the existing spectralspatial deep networks for hyperspectral image classification. Experiments on the hyperspectral data sets show that, compared with some state-of-art deep learning methods, our method achieves better classification results while having smaller network size and less parameters.

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Combined sparse and collaborative representation for hyperspectral target detection

Cited by 225 publications

References 30 publications

A survey on representation-based classification and detection in hyperspectral remote sensing imagery

A survey on representation-based classification and detection in hyperspectral remote sensing imagery

CSA-MSO3DCNN: Multiscale Octave 3D CNN with Channel and Spatial Attention for Hyperspectral Image Classification

Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation

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