高光谱成像
计算机科学
空间分析
遥感
预处理器
计算机视觉
人工智能
棱锥(几何)
模式识别(心理学)
图像分辨率
地质学
数学
几何学
作者
Xin Hu,Yanfei Zhong,Xinyu Wang,Chang Luo,Ji Zhao,Lei Lei,Liangpei Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-17
被引量:23
标识
DOI:10.1109/tgrs.2021.3049292
摘要
In deep learning (DL)-based hyperspectral imagery classification, “spatial patching” is primarily used as a preprocessing for incorporating local spatial information. This operation can help to promote classification accuracy but it is facing new challenges in the unmanned aerial vehicle (UAV)-borne hyperspectral imagery with high spatial and spectral resolutions (H2 imagery). The ground objects’ various spatial scales result in it being challenging to determine the optimal size for the spatial patches. In addition, due to the severe spectral variability and spatial heterogeneity of the H2 imagery, “spatial patching” only exploits the local spatial information and results in serious salt-and-pepper (SP) noise and isolated areas in the classification maps. In this article, to address these issues, a novel spectral patching network (SPNet) with an end-to-end DL architecture is proposed for UAV-borne H2 imagery classification. The “spectral patching” approach is proposed to preserve the global spatial information and almost all the spectral information of the original hyperspectral imagery. An end-to-end deep encoder–decoder network is then constructed based on the spectral patching mechanism, which introduces the deep residual network (ResNet) and atrous spatial pyramid pooling (ASPP) modules to extract multiscale high-level semantic information for the H2 imagery classification. The experimental results obtained with the Wuhan UAV-borne H2 imagery (WHU-Hi) UAV-borne hyperspectral data set demonstrate that SPNet can achieve state-of-the-art accuracy and visualization performance in the classification of H2 imagery.
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