MMO-IG: Multi-Class and Multi-Scale Object Image Generation for Remote Sensing
Chuang Yang, Bingxuan Zhao, Qing Zhou, and Qi Wang

TL;DR
This paper introduces MMO-IG, a novel deep generative model for remote sensing images that synthesizes multi-class, multi-scale objects with labels, improving data augmentation and detection performance.
Contribution
We propose MMO-IG, a multi-class, multi-scale remote sensing image generator using diffusion models, spatial-cross dependency knowledge graph, and structured object distribution instructions.
Findings
Superior generation of dense MMO-supervised RS images.
Enhanced RS object detection performance with pre-trained MMO-IG.
Effective modeling of complex object interdependencies in RS images.
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…
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Taxonomy
TopicsSatellite Image Processing and Photogrammetry · Medical Image Segmentation Techniques · Advanced Image and Video Retrieval Techniques
MethodsDiffusion · Focus
