Multitask Collaborative Multi-modal Remote Sensing Target Segmentation Algorithm

被引：0
|
作者：
Mao, Xiuhua [1 ]
Zhang, Qiang [1 ]
Ruan, Hang [1 ]
Yang, Yuang [1 ]
机构：
[1] (Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China) (National Key Laboratory of Space Integrated Information System
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 08期
关键词：
Deep learning; Elevation estimation; Multi-modal data; Remote sensing images; Semantic segmentation;
D O I：
10.11999/JEIT231267
中图分类号：
学科分类号：
摘要：
The use of semantic segmentation technology to extract high-resolution remote sensing image object segmentation has important application prospects. With the rapid development of multi-sensor technology, the good complementary advantages between multimodal remote sensing images have received widespread attention, and joint analysis of them has become a research hotspot. This article analyzes both optical remote sensing images and elevation data, and proposes a multi-task collaborative model based on multimodal remote sensing data (United Refined PSPNet, UR-PSPNet) to address the issue of insufficient fusion classification accuracy of the two types of data due to insufficient fully registered elevation data in real scenarios. This model extracts deep features of optical images, predicts semantic labels and elevation values, and embeds elevation data as supervised information, to improve the accuracy of target segmentation. This article designs a comparative experiment based on ISPRS, which proves that this algorithm can better fuse multimodal data features and improve the accuracy of object segmentation in optical remote sensing images. © 2024 Science Press. All rights reserved.
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页码：3363 / 3371
页数：8
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