computer-vision-5

Semi-supervised Change Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in semi-supervised-change-detection-5

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LEVIR-CD - 5% labeled data

LEVIR-CD - 10% labeled data

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Use these libraries to find semi-supervised-change-detection-5 models and implementations

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GVLM

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Most implemented papers

BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge

Ali Ismail, M. Awad•Mon Jan 24 2022

BLDNet is presented, a novel graph formulation for building damage change detection and enable learning relationships and representations from both local patterns and non-stationary neighborhoods in a semi-supervised framework with few annotated data.

10

Content

LEVIR-CD - 20% labeled data

LEVIR-CD - 40% labeled data

WHU - 5% labeled data

WHU - 10% labeled data

WHU - 20% labeled data

WHU - 40% labeled data

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Paper Graph

Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images

Vishal M. Patel, W. G. C. Bandara•Sun Apr 17 2022

A semi-supervised CD model is proposed in which an unsupervisedCD loss is formulated in addition to the supervised Cross-Entropy (CE) loss by constraining the output change probability map of a given unlabeled bi-temporal image pair to be consistent under the small random perturbations applied on the deep feature difference map that is obtained by subtracting their latent feature representations.

90 0

Paper Graph

Semi-Supervised Change Detection Of Small Water Bodies Using Rgb And Multispectral Images In Peruvian Rainforests

Kangning Cui, Miles Silman, Seda Camalan, R. Li, V. Pauca, Sarra M. Alqahtani, R. Plemmons, E. N. Dethier, D. Lutz, Raymond Chan•Sat Jun 18 2022

Artisanal and Small-scale Gold Mining (ASGM) is an important source of income for many households, but it can have large social and environmental effects, especially in rainforests of developing countries. The Sentinel-2 satellites collect multispectral images that can be used for the purpose of detecting changes in water extent and quality which indicates the locations of mining sites. This work focuses on the recognition of ASGM activities in Peruvian Amazon rainforests. We tested several semi-supervised classifiers based on Support Vector Machines (SVMs) to detect the changes of water bodies from 2019 to 2021 in the Madre de Dios region, which is one of the global hotspots of ASGM activities. Experiments show that SVM-based models can achieve reasonable performance for both RGB (using Cohen’s κ 0.49) and 6-channel images (using Cohen’s κ 0.71) with very limited annotations. The efficacy of incorporating Lab color space for change detection is analyzed as well.

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Paper Graph

Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

Wayne Zhang, Lei Qi, Litong Feng, Yinghuan Shi, Lihe Yang•Sat Aug 20 2022

This work revisits the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, and presents a dual-stream perturbation technique, enabling two strong views to be simultaneously guided by a common weak view.

408 0

Paper Graph

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