Search Results for author: Shuang He

Found 4 papers, 2 papers with code

WSEBP: A Novel Width-depth Synchronous Extension-based Basis Pursuit Algorithm for Multi-Layer Convolutional Sparse Coding

1 code implementation28 Mar 2022 Haitong Tang, Shuang He, Lingbin Bian, Zhiming Cui, Nizhuan Wang

Specifically, we first propose a novel width-depth synchronous extension-based basis pursuit (WSEBP) algorithm which solves the ML-CSC problem without the limitation of the number of iterations compared to the SOTA algorithms and maximizes the performance by an effective initialization in each layer.

Image Classification

MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification

no code implementations7 Oct 2021 Shuang He, Haitong Tang, Xia Lu, Hongjie Yan, Nizhuan Wang

Specifically, our MSHCNet adopted four parallel streams, which contained G-stream, utilizing the irregular correlation between adjacent land covers in terms of first-order graph in non-Euclidean space; C-stream, adopting convolution operator to learn regular spatial-spectral features in Euclidean space; N-stream, combining first and second order features to learn representative and discriminative regular spatial-spectral features of Euclidean space; S-stream, using GSOP to capture boundary correlations and obtain graph representations from all nodes in graphs of non-Euclidean space.

Hyperspectral Image Classification

RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Imagesbased Semantic Segmentation

no code implementations19 Sep 2021 Shuang He, Xia Lu, Jason Gu, Haitong Tang, Qin Yu, Kaiyue Liu, Haozhou Ding, Chunqi Chang, Nizhuan Wang

For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important.

Semantic Segmentation

CSC-Unet: A Novel Convolutional Sparse Coding Strategy based Neural Network for Semantic Segmentation

1 code implementation1 Aug 2021 Haitong Tang, Shuang He, Xia Lu, Qin Yu, Kaiyue Liu, Hongjie Yan, Nizhuan Wang

Through extensive analysis and experiments, we provided credible evidence showing that the multi-layer convolutional sparse coding block enables semantic segmentation model to converge faster, can extract finer semantic and appearance information of images, and improve the ability to recover spatial detail information.

Semantic Segmentation

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