PROBABILITY FUSION FOR HYPERSPECTRAL AND LIDAR DATA

被引：0

作者：

Ge, Chiru ^{[1
]}

Du, Qian ^{[2
]}

机构：

[1] Shandong Normal Univ, Informat Sci & Engn, Jinan 250358, Peoples R China

[2] Mississippi State Univ, Elect & Comp Engn, Starkville, MS 39762 USA

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

基金：

国家重点研发计划;

关键词：

Hyperspectral image; LiDAR; residual fusion; classification; probability fusion; CLASSIFICATION;

D O I：

10.1109/IGARSS39084.2020.9323750

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a new probability fusion strategy is proposed for hyperspectral and LiDAR data classification, which is inspired by the representation residual fusion strategy in our previous work. Unlike the residual fusion strategy utilizes a collaborative representation classifier, the probability fusion strategy deploys a deep residual network (DRN). This paper compares the two fusion strategies. The experiment results show that the probability fusion strategy with DRN is better than the residual fusion strategy in classification performance.

引用

页码：2675 / 2678

页数：4

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