Change detection of remote sensing images with semi-supervised multilayer perceptron

被引:0
作者
Patra, Swarnajyoti [3 ]
Ghosh, Susmita [3 ]
Ghosh, Ashish [1 ,2 ]
机构
[1] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, India
[2] Indian Stat Inst, Ctr Soft Comp Res, Kolkata 700108, India
[3] Jadavpur Univ, Dept Comp Sci & Engn, Kolkata 700032, India
关键词
semi-supervised learning; remote-sensing; change-detection; multitemporal images; neural network;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
A context-sensitive change-detection technique based on semi-supervised learning with multilayer perceptron is proposed here. In order to take contextual information into account, input patterns are generated considering each pixel of the difference image along with its neighboring pixels. A heuristic technique is suggested to identify a few initial labeled patterns without using ground truth information. The network is initially trained using these labeled data. The unlabeled patterns are iteratively processed by the already trained perceptron to obtain a soft class label. Experimental results, carried out on two multispectral and multitemporal remote sensing images, confirm the effectiveness of the proposed approach.
引用
收藏
页码:429 / 442
页数:14
相关论文
共 50 条
[31]   CHANGE DETECTION OF HIGH-RESOLUTION REMOTE SENSING IMAGE BASED ON SEMI-SUPERVISED SEGMENTATION AND ADVERSARIAL LEARNING [J].
Yang, Shengnan ;
Hou, Shilong ;
Zhang, Yifan ;
Wang, Hongyu ;
Ma, Xiaorui .
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, :1055-1058
[32]   Self-Supervised Change Detection in Multiview Remote Sensing Images [J].
Chen, Yuxing ;
Bruzzone, Lorenzo .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[33]   Deep collaborative learning with class-rebalancing for semi-supervised change detection in SAR images [J].
Hou, Xuan ;
Bai, Yunpeng ;
Xie, Yefan ;
Ge, Huibin ;
Li, Ying ;
Shang, Changjing ;
Shen, Qiang .
KNOWLEDGE-BASED SYSTEMS, 2023, 264
[34]   A semi-supervised change detection for remotely sensed images using ensemble classifier [J].
Roy, Moumita ;
Ghosh, Susmita ;
Ghosh, Ashish .
4TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN COMPUTER INTERACTION (IHCI 2012), 2012,
[35]   Adjacent Teacher: Semi-Supervised Oriented Object Detection Leveraging Adjacent Spatial Consistency Prior in Remote Sensing Images [J].
Xia, Tao ;
Jing, Wei ;
Wang, Qi .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2025, 63
[36]   Remote Sensing Aircraft Image Detection Based on Semi-Supervised Learning [J].
Du Zexing ;
Yin Jinyong ;
Yang Jian .
LASER & OPTOELECTRONICS PROGRESS, 2020, 57 (06)
[37]   SemiSANet: A Semi-Supervised High-Resolution Remote Sensing Image Change Detection Model Using Siamese Networks with Graph Attention [J].
Sun, Chengzhe ;
Wu, Jiangjiang ;
Chen, Hao ;
Du, Chun .
REMOTE SENSING, 2022, 14 (12)
[38]   Co-training with Clustering for the Semi-supervised Classification of Remote Sensing Images [J].
Aydav, Prem Shankar Singh ;
Minz, Sonjharia .
PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION TECHNOLOGIES, IC3T 2015, VOL 2, 2016, 380 :659-667
[39]   A Bias Correction Semi-Supervised Semantic Segmentation Framework for Remote Sensing Images [J].
Zhang, Li ;
Tan, Zhenshan ;
Zheng, Yuzhi ;
Zhang, Guo ;
Zhang, Wen ;
Li, Zhijiang .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2025, 63
[40]   Semi-Supervised Scene Classification for Optical Remote Sensing Images via Label and Embedding Consistency [J].
Xu, Guozheng ;
Zhang, Ze ;
Jiang, Xue ;
Zhou, Yue ;
Liu, Xingzhao .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2024, 21