Practical framework and methodology for high-performance intelligent invariant detection in remote sensing imagery

被引：0

作者：

Ning, Xiaogang ^{[1
]}

Zhang, Hanchao ^{[1
]}

Zhang, Ruiqian ^{[1
]}

机构：

[1] Chinese Academy of Surveying and Mapping, Beijing,100036, China

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2024年 / 53卷 / 06期

关键词：

In addressing the challenges posed by sample category imbalance; limited algorithm applicability; and inadequate knowledge application inherent in traditional change detection techniques; we propose a novel framework for high-reliability; intelligent invariant detection of land classes in remote sensing imagery. This framework employs advanced algorithms to precisely extract stable invariant areas that are typically irrelevant to various tasks; thereby reducing the operational footprint and boosting productivity in practical settings. Commencing with data preprocessing; a sample library tailored to the specifics of invariant detection is developed. Additionally; we introduce a method for invariant detection that utilizes prior information to guide the discrimination between global and local pseudo-changes. This approach leads to the creation of a gridded invariant mask and the introduction of two object-level metrics—compression accuracy and compression range—to assess the framework's performance in terms of accuracy and efficiency. Empirical validation across multiple national regions confirms that this framework not only minimizes the workload associated with manual visual interpretation but also significantly improves the efficiency of data extraction; thus offering a groundbreaking solution for extracting change information from remote sensing data in real-world scenarios. © 2024 SinoMaps Press. All rights reserved;

D O I：

10.11947/j.AGCS.2024.20230405

中图分类号：

学科分类号：

摘要：

引用

页码：1098 / 1112

共 50 条

[21] Remote-powered high-performance strain sensing microsystem
Suster, M
Chaimanonart, N
Guo, J
Ko, WH
Young, DJ
MEMS 2005 MIAMI: TECHNICAL DIGEST, 2005, : 255 - 258
[22] An Efficient Cloud Detection Method for High Resolution Remote Sensing Panchromatic Imagery
Li, Chaowei
Lin, Zaiping
Deng, Xinpu
NINTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING (ICGIP 2017), 2018, 10615
[23] The clustering of high resolution remote sensing imagery
Deng, XJ
Wang, YP
Yun, RS
Peng, HL
IMAGE PROCESSING AND PATTERN RECOGNITION IN REMOTE SENSING, 2003, 4898 : 180 - 187
[24] ClouDet: A Dilated Separable CNN-Based Cloud Detection Framework for Remote Sensing Imagery
Guo, Hongwei
Bai, Hongyang
Qin, Weiwei
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 9743 - 9755
[25] High-performance technology for indexing of high volumes of Earth remote sensing data
Strotov, Valery V.
Taganov, Alexander I.
Kolesenkov, Aleksandr N.
Kostrov, Boris V.
HIGH-PERFORMANCE COMPUTING IN GEOSCIENCE AND REMOTE SENSING VII, 2017, 10430
[26] Cascaded Object Detection Algorithm in Remote Sensing Imagery
Zhang X.
Li C.
Xu J.
Xie J.
Cui Z.
Yang J.
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2021, 33 (10): : 1524 - 1531
[27] CDUNet: Cloud Detection UNet for Remote Sensing Imagery
Hu, Kai
Zhang, Dongsheng
Xia, Min
REMOTE SENSING, 2021, 13 (22)
[28] YOLOrs: Object Detection in Multimodal Remote Sensing Imagery
Sharma, Manish
Dhanaraj, Mayur
Karnam, Srivallabha
Chachlakis, Dimitris G.
Ptucha, Raymond
Markopoulos, Panos P.
Saber, Eli
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 1497 - 1508
[29] Anomaly Detection from Hyperspectral Remote Sensing Imagery
Guo, Qiandong
Pu, Ruiliang
Cheng, Jun
GEOSCIENCES, 2016, 6 (04)
[30] LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images
Tian, Yang
Wang, Xuan
Zhu, Shengjie
Xu, Fang
Liu, Jinghong
REMOTE SENSING, 2023, 15 (17)

← 1 2 3 4 5 →