Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

Luo, Fulin; Hong, Huixiao; Duan, Yule; Liu, Shiyuan; Liao, Y. Gene

doi:10.3390/rs9080790

Cited by 140 publications

(74 citation statements)

References 34 publications

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“…The number of basis materials is less than energy channels. This indicates the spectral images contain a large amount of spectral redundancy, and the images obtained from different channels are highly correlated [40,41]. Second, as the multi-energy projection datasets are obtained from the same patient by different energy thresholds, images reconstructed across spectral dimension have different attenuation coefficients but share the same image structures.…”

Section: C Non-local Similar Cubes Matchingmentioning

confidence: 99%

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

Liu

Zhang

et al. 2019

IEEE Trans. Med. Imaging

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Spectralcomputed tomography (CT) reconstructs material-dependent attenuation images from the projections of multiple narrow energy windows which is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection datasets usually have lower signal-noise-ratios (SNR). Very recently, a spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectral similarities for spectral CT. This method constructs a group by clustering up a series of non-local spatial-spectral cubes. The small size of spatial patches for such a group makes the SSCMF fail to fully encode the sparsity and low-rank properties. The hard-thresholding and collaboration filtering in the SSCMF also cause difficulty in recovering the image features and spatial edges. While all the steps are operated on 4-D group, the huge computational cost and memory load might not be affordable in practice. To avoid the above limitations and further improve image quality, we first formulate a non-local cube-based tensor instead of group to encode the sparsity and low-rank properties. Then, as a new regularizer, the Kronecker-Basis-Representation (KBR) tensor factorization is employed into a basic spectral CT reconstruction model to enhance the capability of image feature extraction and spatial edge preservation, generating a non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman method is adopted to solve the NLCTF model. Both numerical simulations and preclinical mouse studies are performed to validate and evaluate the NLCTF algorithm. The results show that the NLCTF method outperforms other state-ofthe-art competing algorithms.

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Section: C Non-local Similar Cubes Matchingmentioning

confidence: 99%

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

Liu

Zhang

et al. 2019

IEEE Trans. Med. Imaging

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“…1 shows the mouse thorax phantom, which contains three materials (i.e., bone, soft tissue and iodine contrast agent). A polychromatic 50 kVp x-ray source is divided into 4 energy bins ( [16,22) keV, [22,25) keV, [25,28) keV, [28,50] keV), as shown in Fig. 2.…”

Section: A Numerical Simulationsmentioning

confidence: 99%

Improved Material Decomposition With a Two-Step Regularization for Spectral CT

Chen

Vardhanabhuti

et al. 2019

IEEE Access

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One of the advantages of spectral computed tomography (CT) is it can achieve accurate material components using the material decomposition methods. The image-based material decomposition is a common method to obtain specific material components, and it can be divided into two steps: image reconstruction and post material decomposition. To obtain accurate material maps, the image reconstruction method mainly focuses on improving image quality by incorporating regularization priors. Very recently, the regularization priors are introduced into the post material decomposition procedure in the iterative image-based methods. Since the regularization priors can be incorporated into image reconstruction and post image-domain material decomposition, the performance of regularization by combining these two cases is still an open problem. To realize this goal, the material accuracy from those steps are first analyzed and compared. Then, to further improve the accuracy of decomposition materials, a two-step regularization based method is developed by incorporating priors into image reconstruction and post material decomposition. Both numerical simulation and preclinical mouse experiments are performed to demonstrate the advantages of the two-step regularization based method in improving material accuracy.

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“…In fact, all processing steps in TPOBDL are based on image superpixels. Since a superpixel is a local set of homogeneous pixels, superpixels can reflect the local spatial context [27,28,29]. Therefore, this approach can overcome the problems caused by operations involving rectangular patches, such as introducing artefacts and uncertainty in the classification.…”

Section: Introductionmentioning

confidence: 99%

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Zhang

Atkinson

et al. 2020

Remote Sensing

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Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase objectbased deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighboring pixels into segmented into superpixel objects (from pixels) such as to exploit local spatial context. Two phases are designed in the methodology: 1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. 2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

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Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery

Cited by 140 publications

References 34 publications

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

Improved Material Decomposition With a Two-Step Regularization for Spectral CT

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

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