计算机科学
人工智能
像素
多光谱图像
全色胶片
稳健性(进化)
计算机视觉
传感器融合
图像分辨率
模式识别(心理学)
数据挖掘
生物化学
基因
化学
作者
Hao Zhu,Wenping Ma,Lingling Li,Licheng Jiao,Shuyuan Yang,Biao Hou
标识
DOI:10.1016/j.inffus.2019.12.013
摘要
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.
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