Semi-supervised label propagation for multi-source remote sensing image change detection

Hao, Fan; Ma, Zongfang; Tian, Hongpeng; Wang, Hao; Wu, Di

doi:10.1016/j.cageo.2022.105249

Cited by 15 publications

(9 citation statements)

References 34 publications

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“…For instance, SAR data excel in all-weather conditions, penetrating clouds to complement optical imagery; LiDAR data provides detailed information about terrain and surface elevation, enhancing CD accuracy in diverse terrain regions. There are already some DL-based CD studies leveraging multimodal data [157,[233][234][235][236]. For instance, Li et al [237] proposed a GAN and CNN-based network for optical and SAR image CD, using GANs to align optical and SAR images into the same feature space, followed by supervised CNN for CD.…”

Section: Multimodal CDmentioning

confidence: 99%

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

Wang,

Zhang,

Gao

et al. 2024

Remote Sensing

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Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field.

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Section: Multimodal CDmentioning

confidence: 99%

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

Wang,

Zhang,

Gao

et al. 2024

Remote Sensing

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“…Using large-scale labeled aerial image dataset to train detectors can intuitively enhance robustness, but manually labeling objects is time-consuming and labor-intensive 25 . Data augmentation expands the dataset by providing a diverse view of the sample.…”

Section: Related Workmentioning

confidence: 99%

Expert teacher based on foundation image segmentation model for object detection in aerial images

Yu,

Sun,

Cheng

2023

Sci Rep

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Despite the remarkable progress of general object detection, the lack of labeled aerial images limits the robustness and generalization of the detector. Teacher–student learning is a feasible solution on natural image domain, but few works focus on unlabeled aerial images. Inspired by foundation models with the powerful generalization in computer vision field, we propose an expert teacher framework based on foundation image segmentation model called ET-FSM. Our approach provides the performance gains for the student detector by generating high-quality pseudo-labels for unlabeled aerial images. In the ET-FSM, we design the binary detector with expert guidance mechanism to sufficiently leverage the extra knowledge obtained from the foundation image segmentation model, which accurately detects object positions in the complex backgrounds. Also, we present the momentum contrast classification module to distinguish confused object categories in aerial images. To demonstrate the effectiveness of the proposed method, we construct an unlabeled aerial image dataset covering various scenes. The experiments are conducted on diverse types of student detectors. The results show that the proposed approach achieves superior performance compared to existing methods, and allows the student detector to achieve fully supervised performance with much less labeled aerial images. Our dataset and code are available at https://github.com/cq100/ET-FSM.

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“…Sohn et al [9] proposed an integrated classifier to give unlabeled images with pseudo-labels and used them to improve the performance of a model on image classification tasks. Hao et al [33] inferred pseudo-labels for unlabeled data with graph-based label propagation and promoted the ability of image change detection. Besides classification, semi-supervised learning is also used in many other applications, e.g., object detection [10,11], motion analysis [34], and multi-view models [35].…”

Section: Semi-supervised Learningmentioning

confidence: 99%

“…Besides classification, semi-supervised learning is also used in many other applications, e.g., object detection [10,11], motion analysis [34], and multi-view models [35]. By marking pseudolabels to the sample without labels, all the methods mentioned above [8][9][10][11][32][33][34][35] leverage the unlabeled data to help the updating of the learning-based network and improve the performance of recognition. However, the reliability or uncertainty of the marked pseudo-labels may influence the efficiency of the learning-based network.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

Semi-Supervised Group Emotion Recognition Based on Contrastive Learning

et al. 2022

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The performance of all learning-based group emotion recognition (GER) methods depends on the number of labeled samples. Although there are lots of group emotion images available on the Internet, labeling them manually is a labor-intensive and cost-expensive process. For this reason, datasets for GER are usually small in size, which limits the performance of GER. Considering labeling manually is challenging, using limited labeled images and a large number of unlabeled images in the network training is a potential way to improve the performance of GER. In this work, we propose a semi-supervised group emotion recognition framework based on contrastive learning to learn efficient features from both labeled and unlabeled images. In the proposed method, the unlabeled images are used to pretrain the backbone by a contrastive learning method, and the labeled images are used to fine-tune the network. The unlabeled images are then given pseudo-labels by the fine-tuned network and used for further training. In order to alleviate the uncertainty of the given pseudo-labels, we propose a Weight Cross-Entropy Loss (WCE-Loss) to suppress the influence of the samples with unreliable pseudo-labels in the training process. Experiment results on three prominent benchmark datasets for GER show the effectiveness of the proposed framework and its superiority compared with other competitive state-of-the-art methods.

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Semi-supervised label propagation for multi-source remote sensing image change detection

Cited by 15 publications

References 34 publications

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

Advances and Challenges in Deep Learning-Based Change Detection for Remote Sensing Images: A Review through Various Learning Paradigms

Expert teacher based on foundation image segmentation model for object detection in aerial images

Semi-Supervised Group Emotion Recognition Based on Contrastive Learning

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