Shenzhen University

Shenzhen Institutes of Advanced Technology

Sen Jia

Jie Hu

Lin Deng

Xin Liu

Hairong Zheng

Dong Liang

Bin Deng

Haifeng Wang

Qingquan Li

Jiasong Zhu

Yao Xie

Linlin Shen

Provided are an image fusion classification method and device. The method includes that: a three-dimensional weight matrix of a hyperspectral image is obtained by use of a Support Vector Machine (SVM) classifier (); superpixel segmentation is performed on the hyperspectral image to obtain K superpixel images, K being a positive integer (); the three-dimensional weight matrix is regularized by use of a superpixel-image-based segmentation method to obtain a regular matrix (); and a class that a sample belongs to is determined according to the regular matrix ().

Image classification method and device

There are provided a parallel rapid imaging method and device based on a complex number conjugate symmetry of multi-channel coil data and nonlinear GRAPPA image reconstruction, and a medium. The imaging method includes: obtaining virtual conjugate coil data by expanding the actual multi-channel coil data; combining actual multi-channel coil data and virtual multi-channel coil data to obtain a linear data term and a nonlinear data term; calibrating weighting factors of the linear data term and the nonlinear data term by using combined low-frequency full-sampling data (margins of the low-frequency full-sampling data includes parts of high-frequency data); reconstructing data which is under-sampled in a high-frequency region according to the calibrated weighting factors; fusing the low-frequency full-sampling data and the reconstructed data for the high-frequency region.

Imaging method and device for nonlinear parallel magnetic resonance image reconstruction, and medium

The present invention provides a Gabor cube feature selection-based classification method for hyperspectral remote sensing images, comprising the following steps: generating three-dimensional Gabor filters according to set frequency and direction parameter values; convoluting hyperspectral remote sensing images with the three-dimensional Gabor filters to obtain three-dimensional Gabor features; selecting three-dimensional Gabor features, classification contribution degrees to various classes of which meet preset requirements, from the three-dimensional Gabor features; and classifying the hyperspectral remote sensing images by a multi-task joint sparse representation-based classification means by using the selected three-dimensional Gabor features. The present invention is based on the three-dimensional Gabor features, and the used three-dimensional Gabor features contain rich local change information of a signal and are competent in feature characterizing. Using a Fisher discriminant criterion not only makes full use of high-level semantics hidden among the features, but also eliminates redundant information and reduces the classification time complexity.

Gabor cube feature selection-based classification method and system for hyperspectral remote sensing images

The present invention provides a method for extracting and classifying features of hyperspectral remote sensing image, including: an sampling step, a binarizing step, a coding step, a statistical calculating step, a concatenating step, and a classifying step. The present invention further provides a system for extracting and classifying features of hyperspectral remote sensing image. The technical solution provided by the present invention can make full use of the contextual relationship between the spectral domain and the spatial domain in a hyperspectral remote sensing image by extending two-dimensional LBPs into three-dimensional LBPs, and has good robustness to noise by introducing a relaxation threshold discrimination operation. Furthermore, the rotation-invariant three-dimensional LBP model provided by the present invention takes account of the essential characteristics of the hyperspectral remote sensing image, and therefore the present solution has advantages that it is targeted, simple in operation and high in calculation efficiency.

Method and system for extracting and classifying features of hyperspectral remote sensing image

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Guangdong, China

Sen Jia