Fidelity-Diversity-Consistency (FDC): Data Pruning for Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2608.21754v1
• Date: Sat, 22 Aug 2026 03:33:03 GMT
• Title: Fidelity-Diversity-Consistency (FDC): Data Pruning for Remote Sensing Change Detection
• Abstract summary: We benchmark six representative DP methods across building- and forest-change datasets, CNN- and transformer-based models, and three pruning budgets.<n>Even the strongest evaluated baseline, Feature Diversity, is matched or exceeded by $sim$33% of randomly sampled subsets.<n>We translate these findings into Fidelity-Diversity-Consistency (FDC), a simple two-stage pruning method.
• Score: 1.771620608771373
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite the success of data pruning (DP) in reducing training data sizes and improving downstream model performance in classification and segmentation tasks, its potential in remote sensing change detection remains unexplored. For the first time, we benchmark six representative DP methods across building- and forest-change datasets, CNN- and transformer-based models, and three pruning budgets, and show that existing baselines yield no reliable advantage over random selection. Notably, even the strongest evaluated baseline, Feature Diversity, is matched or exceeded by $\sim$33\% of randomly sampled subsets. To understand the underlying mechanism, we conduct a systematic regression study over 540 randomly sampled data subsets, characterizing each with four descriptors covering label statistics, image diversity, and feature-space geometry. Random Forest models show that \emph{change distribution fidelity} is the most prominent factor in determining the quality of change detection data subsets, a property absent from the existing pruning literature. Our analyses further show that pixel-wise image diversity and label-feature consistency are secondary factors. We translate these findings into Fidelity-Diversity-Consistency (FDC), a simple two-stage pruning method that shows consistent improvements over existing baselines across change detection benchmarks and backbones, especially at lower pruning ratios. Code is available at \href{https://github.com/ddydyd32/fidelity-diversity-consistency}{https://github.com/ddydyd32/fidelity-diversity-consistency}.

Related papers

• ControlledShifts: Towards Standardizing Robustness Evaluation in Trajectory Prediction Under Distribution Shifts [12.370065954075299]
  Trajectory prediction is central to safety in autonomous driving.<n>Many methods attempt to mitigate distribution shift degradation through data-centric or test-time adaptation approaches.<n>We introduce ControlledShifts, a framework that systematically re-splits existing trajectory datasets into in-distribution and out-of-distribution partitions.
  arXiv  Detail & Related papers  (2026-08-18T15:16:52Z)
• PISA: A Pseudo-Individual Source-Domain Feature Adaptation Framework for Test-Time Open-Vocabulary Object Detection [3.403013334650533]
  PISA is a source-free OVOD-TTA method seamlessly integrated into open-vocabulary visual backbones.<n>We show PISA substantially improves both the localization precision and the category recognition accuracy of the original models.
  arXiv  Detail & Related papers  (2026-08-14T09:47:44Z)
• SEED: Targeted Data Selection by Weighted Independent Set [76.68391670109433]
  We develop a robust and scalable data selection pipeline dubbed SEED.<n>SEED consistently outperforms state-of-the-art methods on instruction tuning, visual instruction tuning, and semantic segmentation.
  arXiv  Detail & Related papers  (2026-05-15T07:26:54Z)
• Statistically-Guided Dual-Domain Meta-Learning with Adaptive Multi-Prototype Aggregation for Distributed Fiber Optic Sensing [11.719957656139824]
  We propose a novel meta-learning framework, DUPLE, for cross-deployment DFOS activity identification.<n>First, a dual-domain multi-prototype learner fuses temporal and frequency domain features, enhancing the model's generalization ability under signal distribution shifts.<n>Second, a Statistical Guided Network (SGN) infers domain importance and prototype sensitivity from raw statistical features, providing data-driven prior information for learning in unlabeled or unseen domains.<n>Third, a query-aware prototype aggregation module adaptively selects and combines relevant prototypes, thereby improving classification performance even with limited data.
  arXiv  Detail & Related papers  (2025-11-22T03:39:13Z)
• DCFS: Continual Test-Time Adaptation via Dual Consistency of Feature and Sample [22.84826831249534]
  Continual test-time adaptation aims to continuously adapt a pre-trained model to a stream of target domain data without accessing source data.<n>We propose DCFS, a novel CTTA framework that introduces dual-path feature consistency and confidence-aware sample learning.
  arXiv  Detail & Related papers  (2025-08-28T07:57:54Z)
• Feature-Based Instance Neighbor Discovery: Advanced Stable Test-Time Adaptation in Dynamic World [7.9346432903946535]
  Deep neural networks still suffer performance declines under distribution shifts between training and test domains.<n>We propose Feature-based Instance Neighbor Discovery (FIND), which comprises three key components: Layer-wise Feature Disentanglement (LFD), Feature Aware Batch Normalization (FABN) and Selective FABN (S-FABN)<n>Experiments demonstrate FIND significantly outperforms existing methods, achieving a 30% accuracy improvement in dynamic scenarios.
  arXiv  Detail & Related papers  (2025-06-07T12:45:49Z)
• Robust Outlier Rejection for 3D Registration with Variational Bayes [70.98659381852787]
  We develop a novel variational non-local network-based outlier rejection framework for robust alignment. We propose a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation.
  arXiv  Detail & Related papers  (2023-04-04T03:48:56Z)
• Explaining Cross-Domain Recognition with Interpretable Deep Classifier [100.63114424262234]
  Interpretable Deep (IDC) learns the nearest source samples of a target sample as evidence upon which the classifier makes the decision. Our IDC leads to a more explainable model with almost no accuracy degradation and effectively calibrates classification for optimum reject options.
  arXiv  Detail & Related papers  (2022-11-15T15:58:56Z)
• Semantic Change Detection with Asymmetric Siamese Networks [71.28665116793138]
  Given two aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem is vital in many earth vision related tasks, such as precise urban planning and natural resource management. We present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures.
  arXiv  Detail & Related papers  (2020-10-12T13:26:30Z)
• Uncertainty Inspired RGB-D Saliency Detection [70.50583438784571]
  We propose the first framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose a generative architecture to achieve probabilistic RGB-D saliency detection. Results on six challenging RGB-D benchmark datasets show our approach's superior performance in learning the distribution of saliency maps.
  arXiv  Detail & Related papers  (2020-09-07T13:01:45Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.