MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing

• URL: http://arxiv.org/abs/2412.13684v1
• Date: Wed, 18 Dec 2024 10:19:12 GMT
• Title: MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing
• Authors: Chuang Yang, Bingxuan Zhao, Qing Zhou, Qi Wang,
• Abstract summary: MMO-IG is designed to generate RS images with supervised object labels from global and local aspects simultaneously. Considering the complex interdependencies among MMOs, we construct a spatial-cross dependency knowledge graph. Our MMO-IG exhibits superior generation capabilities for RS images with dense MMO-supervised labels.
• Score: 12.491684385808902
• License:
• Abstract: The rapid advancement of deep generative models (DGMs) has significantly advanced research in computer vision, providing a cost-effective alternative to acquiring vast quantities of expensive imagery. However, existing methods predominantly focus on synthesizing remote sensing (RS) images aligned with real images in a global layout view, which limits their applicability in RS image object detection (RSIOD) research. To address these challenges, we propose a multi-class and multi-scale object image generator based on DGMs, termed MMO-IG, designed to generate RS images with supervised object labels from global and local aspects simultaneously. Specifically, from the local view, MMO-IG encodes various RS instances using an iso-spacing instance map (ISIM). During the generation process, it decodes each instance region with iso-spacing value in ISIM-corresponding to both background and foreground instances-to produce RS images through the denoising process of diffusion models. Considering the complex interdependencies among MMOs, we construct a spatial-cross dependency knowledge graph (SCDKG). This ensures a realistic and reliable multidirectional distribution among MMOs for region embedding, thereby reducing the discrepancy between source and target domains. Besides, we propose a structured object distribution instruction (SODI) to guide the generation of synthesized RS image content from a global aspect with SCDKG-based ISIM together. Extensive experimental results demonstrate that our MMO-IG exhibits superior generation capabilities for RS images with dense MMO-supervised labels, and RS detectors pre-trained with MMO-IG show excellent performance on real-world datasets.

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