Dynamic Bit-Plane Augmentation Framework for Enhancing Data Quality and Robustness in Remote Sensing - Weipeng Jing; Tianyi Liu; Lina Wang; Peilun Kang; Chao Li; Hailin Feng (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
In artificial intelligence (AI)-driven remote sensing, data quality issues critically affect the generalization and reliability of deep learning models. To address this problem, we propose the dynamic attention bit-plane augmentation (DAPW) framework, designed for task-relevant and structure-adaptive augmentation in multiscene remote sensing tasks. At the core of DAPW is an adaptive selection mechanism that utilizes a dynamic perception window and entropy-gradient-contrast metrics to identify informative bit-plane regions. This approach effectively reduces redundancy and enhances the structural expressiveness of augmented data. To further improve robustness and intraclass diversity, we incorporate a lightweight tunable augmentation strategy that introduces controlled appearance-level perturbations while maintaining semantic integrity. The integration of these two components enables a unified augmentation pipeline, which is validated across three representative tasks to demonstrate cross-domain generalizability. To our knowledge, this is the first study to apply bit-plane (BP) augmentation to tree counting. Extensive experiments show consistent improvements across diverse scenarios: on the London Tree Counting dataset ( $E_MAE$ = 19.9, $E_F1$ = 72.4%), DOTA object detection (mAP = 83.6%), and CIFAR-100 classification (Top-1 = 84.17%). Compared to prior approaches such as BP information recombination model and conventional BP recombination, DAPW offers systemic advances in adaptive region selection, redundancy suppression, and alignment between augmentation strategy and downstream tasks, providing a more efficient and interpretable solution for improving data quality and model performance in AI-based remote sensing.