Style Alignment based Dynamic Observation Method for UAV-View Geo-localization
Not on the map. Why?
Abstract (arXiv)
The task of UAV-view geo-localization is to estimate the localization of a query satellite/drone image by matching it against a reference dataset consisting of drone/satellite images. Though tremendous strides have been made in feature alignment between satellite and drone views, vast differences in both inter and intra-class due to changes in viewpoint, altitude, and lighting remain a huge challenge. In this paper, a style alignment based dynamic observation method for UAV-view geo-localization is proposed to meet the above challenges from two perspectives: visual style transformation and surrounding noise control. Specifically, we introduce a style alignment strategy to transfrom the diverse visual style of drone-view images into a unified satellite images visual style. Then a dynamic observation module is designed to evaluate the spatial distribution of images by mimicking human observation habits. It is featured by the hierarchical attention block (HAB) with a dual-square-ring stream structure, to reduce surrounding noise and geographical deformation. In addition, we propose a deconstruction loss to push away features of different geo-tags and squeeze knowledge from unmatched images by correlation calculation. The experimental results demonstrate the state-of-the-art performance of our model on benchmarked datasets. In particular, when compared to the prior art on University-1652, our results surpass the best of them (FSRA), while only requiring 2x fewer parameters. Code will be released at https://github.com/Xcco1/SADOM
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 41 references that are in the map. First posted on arXiv 3 Jul 2024.
Builds on
27 of 41 references.
References · 27
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2017Squeeze-and-Excitation Networkscited by 4,763
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
- 2018CBAM: Convolutional Block Attention Modulecited by 2,463
- 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
- 2020Deep Learning for Person Re-identification: A Survey and Outlookcited by 547
- 2017Fine-tuning CNN Image Retrieval with No Human Annotationcited by 497
- 2017Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)cited by 472
- 2019Lending Orientation to Neural Networks for Cross-view Geo-localizationcited by 179
- 2019A Strong Baseline and Batch Normalization Neck for Deep Person Re-identificationcited by 154
- 2020University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localizationcited by 152
15 more
- 2017Dual-Path Convolutional Image-Text Embeddings with Instance Losscited by 145
- 2020Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matchingcited by 125
- 2020Graph Convolutional Networks for Hyperspectral Image Classificationcited by 113
- 2020Each Part Matters: Local Patterns Facilitate Cross-view Geo-localizationcited by 94
- 2018An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixingcited by 65
- 2017AlignedReID: Surpassing Human-Level Performance in Person Re-Identificationcited by 61
- 2022SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellitecited by 61
- 2022A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localizationcited by 55
- 2022Vision-Based UAV Self-Positioning in Low-Altitude Urban Environmentscited by 46
- 2022Multiple-environment Self-adaptive Network for Aerial-view Geo-localizationcited by 37
- 2022Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filterscited by 17
- 2022Geo-Localization via Ground-to-Satellite Cross-View Image Retrievalcited by 16
- 2021InsCLR: Improving Instance Retrieval with Self-Supervisioncited by 11
- 2022Parallel Structure from Motion for UAV Images via Weighted Connected Dominating Setcited by 10
- 2022Rank-Based Filter Pruning for Real-Time UAV Trackingcited by 8
Built on by
Citing papers per year: 2024: 1.
1 paper.