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Your search keyword '"Hou, Biao"' showing total 2 results
2 results on '"Hou, Biao"'

1. Hyperspectral image classification based on spatial and spectral kernels generation network.

Author

Ma, Wenping, Ma, Haoxiang, Zhu, Hao, Li, Yating, Li, Longwei, Jiao, Licheng, and Hou, Biao

Subjects
*DEEP learning, *CONVOLUTIONAL neural networks, *SPECTRAL imaging, *CLASSIFICATION
Abstract

• A spatial kernel generation module is proposed. • A spectral kernel generation module is proposed. • A multi-scale spectral channel attention mechanism is proposed. • A strategy for feature fusion through convolution is proposed. • Our network structure has better classification results than the state-of-the-art. With the widespread use of deep learning methods, more and more classification models based on hyperspectral images (HSI) have been continuously proposed. However, due to the characteristics of high dimensionality and low resolution of HSI, the traditional convolutional neural network (CNN) model cannot effectively process it. In this paper, we propose a classification network, called spatial and spectral kernels generation network (SSKNet), to generate spatial and spectral convolution kernels based on image characteristics, and use them to replace the traditional initialization convolution kernels. We can obtain representative kernels with stronger spatial correlation with the region segmentation, clustering, and mapping operations of the spatial kernels generation module (SaKG Module). Simultaneously, we also propose a spectral kernels generation module (SeKG module), which integrates the multi-scale correlation characteristics of different bands into the spectral attention mechanism, making the generated spectral kernels more accurate. Combining the spatial and spectral kernels allows the network to extract salient features and achieve feature fusion efficiently. Experimental results based on multiple HSI data sets show that the proposed method has better classification accuracy and generalization performance than existing methods. [ABSTRACT FROM AUTHOR]

Published
2021
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2. A Dual–Branch Attention fusion deep network for multiresolution remote–Sensing image classification.

Author

Zhu, Hao, Ma, Wenping, Li, Lingling, Jiao, Licheng, Yang, Shuyuan, and Hou, Biao

Subjects
*REMOTE-sensing images, *CLASSIFICATION, *REMOTE sensing, *DEEP learning, *IMAGE fusion, *MACHINE learning
Abstract

• A dual branch attention fusion deep network for classification is proposed. • An adaptive center offset sampling strategy for each patch is proposed. • Two attention based modules to highlight the advantages of two different data. • The feature level fusion is achieved by the proposed network. In recent years, with the diversification of acquisition methods of very high resolution panchromatic (PAN) and multispectral (MS) remote sensing images, multiresolution remote sensing classification has become a research hotspot. In this paper, from the perspective of data–driven deep learning, we design a dual–branch attention fusion deep network (DBAF–Net) for the multiresolution classification. It aims to integrate the feature–level fusion and classification into an end–to–end network model. In the process of establishing a training sample library, unlike the traditional pixel–centric sampling strategy with fixed patch size, we propose an adaptive center–offset sampling strategy (ACO–SS), which allows each patch to adaptively determine the neighborhood range by finding the texture structure of the pixel to be classified. And the neighborhood range is not symmetrical with this pixel, we expect to capture the neighborhood information that is more conducive to its classification. In network structure, based on the captured patches by ACO–SS, we design a spatial attention module (SA–module) for PAN data and a channel attention module (CA–module) for MS data, thus highlighting the spatial resolution advantages of PAN data and the multi–channel advantages of MS data, respectively. Then these two features are interfused to improve and strengthen the fusion features in both spatial and channel. The quantitative and qualitative experimental results verify the robustness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2020
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