Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling

TL;DR

This paper introduces YOLO-DCAP, an improved satellite object localization model that uses multi-scale dilated residual convolutions and attention-aided spatial pooling, significantly outperforming existing methods across multiple datasets.

Contribution

The paper proposes YOLO-DCAP, a novel model combining MDRC and AaSP modules to enhance satellite object localization, demonstrating superior performance over existing models.

Findings

20.95% average mAP50 improvement over YOLO base model

32.23% average IoU improvement over YOLO base model

Consistent performance gains across three satellite datasets

Abstract

Object localization in satellite imagery is particularly challenging due to the high variability of objects, low spatial resolution, and interference from noise and dominant features such as clouds and city lights. In this research, we focus on three satellite datasets: upper atmospheric Gravity Waves (GW), mesospheric Bores (Bore), and Ocean Eddies (OE), each presenting its own unique challenges. These challenges include the variability in the scale and appearance of the main object patterns, where the size, shape, and feature extent of objects of interest can differ significantly. To address these challenges, we introduce YOLO-DCAP, a novel enhanced version of YOLOv5 designed to improve object localization in these complex scenarios. YOLO-DCAP incorporates a Multi-scale Dilated Residual Convolution (MDRC) block to capture multi-scale features at scale with varying dilation rates, and an Attention-aided Spatial Pooling (AaSP) module to focus on the global relevant spatial regions, enhancing feature selection. These structural improvements help to better localize objects in satellite imagery. Experimental results demonstrate that YOLO-DCAP significantly outperforms both the YOLO base model and state-of-the-art approaches, achieving an average improvement of 20.95% in mAP50 and 32.23% in IoU over the base model, and 7.35% and 9.84% respectively over state-of-the-art alternatives, consistently across all three satellite datasets. These consistent gains across all three satellite datasets highlight the robustness and generalizability of the proposed approach. Our code is open sourced at https://github.com/AI-4-atmosphere-remote-sensing/satellite-object-localization.