Semi-supervised Change Detection
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Benchmarks
WHU - 10% labeled data
WHU - 20% labeled data
WHU - 40% labeled data
WHU - 5% labeled data
Most implemented
SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector
UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation
C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images
Papers
HSACNet: Hierarchical Scale-Aware Consistency Regularized Semi-Supervised Change Detection
Semi-supervised change detection (SSCD) aims to detect changes between bi-temporal remote sensing images by utilizing limited labeled data and abundant unlabeled data. Existing methods struggle in complex scenarios, exhi…
Change Detectionparameter-efficient fine-tuningSemi-supervised Change DetectionGTPC-SSCD: Gate-guided Two-level Perturbation Consistency-based Semi-Supervised Change Detection
Semi-supervised change detection (SSCD) utilizes partially labeled data and abundant unlabeled data to detect differences between multi-temporal remote sensing images. The mainstream SSCD methods based on consistency reg…
Change DetectionSemi-supervised Change DetectionAdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on Pseudo-Label Evaluation
Change Detection (CD) is an essential field in remote sensing, with a primary focus on identifying areas of change in bi-temporal image pairs captured at varying intervals of the same region by a satellite. The data anno…
Change DetectionPseudo LabelSemi-supervised Change DetectionUniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation (SSS) aims at learning rich visual knowledge from cheap unlabeled images to enhance semantic segmentation capability. Among recent works, UniMatch improves its precedents tremendousl…
Semantic SegmentationSemi-supervised Change DetectionSemi-Supervised Semantic SegmentationCross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection
Semi-supervised change detection (SSCD) utilizes partially labeled data and a large amount of unlabeled data to detect changes. However, the transformer-based SSCD network does not perform as well as the convolution-base…
Change DetectionDecoderSemi-supervised Change DetectionSemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector
Change Detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is labor-intensive and costly, especially for multi-temporal images, which req…
Change DetectionLanguage ModelingLanguage ModellingSemi-supervised Change Detection