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Layout-Controlled Synthetic Data Generation for Remote Sensing Object Detection
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DOI：10.1109/TGRS.2026.3679408.png)
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Abstract
En 中文
High-performance remote sensing object detection typically requires vast datasets with annotations, but the collection and labeling of large-scale data is a costly and labor-intensive process. While generative models have shown promise in addressing the need for large-scale datasets, they heavily rely on high-quality labeled data and often overlook crucial aspects, such as the interobject relationships and overall quality of the synthetic data. To this end, we propose layout-controlled synthetic data generation (LSDGen), a novel approach for generating high-fidelity remote sensing imagery for object detection. LSDGen consists of three key stages: 1) relation-constrained layout generation (RLGen), which constructs a category association matrix from benchmark data to generate both main and subcategories, as well as their spatial configurations, without relying on bounding-box annotations; 2) image synthesis via a layout-conditional diffusion model (LCDM), where the generated layout guides a diffusion model to produce high-quality remote sensing images; and 3) instance-centered synthetic data filtering (ISDF), which evaluates the semantic consistency of individual instances in the synthesized images by calculating instance-level cosine similarity scores between visual and textual representations, filtering out low-confidence samples based on the average semantic score. Experimental results on the object DetectIon in Optical Remote sensing images (DIOR), oriented object detection benchmark based on DIOR dataset (DIOR-R), and high-resolution ship corpus (HRSC) datasets demonstrate that our method significantly improves the detection performance of existing remote sensing image object detection models, outperforming state-of-the-art annotation-dependent synthesis approaches.
Keywords:
Object detection
remote sensing images
synthetic data filtering
synthetic data generation
Journal
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