# Relation Network for Multi-label Aerial Image Classification

arXiv: https://arxiv.org/abs/1907.07274 · HTML: https://mlsanity.com/paper/1907.07274/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 26 of 26)

- [SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding](https://mlsanity.com/paper/2406.10100/) (2024)
- [Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification](https://mlsanity.com/paper/2207.07189/) (2022)
- [SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models](https://mlsanity.com/paper/2304.11619/) (2023)
- [MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images](https://mlsanity.com/paper/2104.02846/) (2021)
- [Rethinking Crowdsourcing Annotation: Partial Annotation with Salient Labels for Multi-Label Image Classification](https://mlsanity.com/paper/2109.02688/) (2021)
- [AiTLAS: Artificial Intelligence Toolbox for Earth Observation](https://mlsanity.com/paper/2201.08789/) (2022)
- [All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification](https://mlsanity.com/paper/2205.03371/) (2022)
- [Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training](https://mlsanity.com/paper/2205.08416/) (2022)
- [Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains](https://mlsanity.com/paper/2301.04494/) (2023)
- [Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks](https://mlsanity.com/paper/2104.11200/) (2021)
- [MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection](https://mlsanity.com/paper/2212.02804/) (2022)
- [Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification](https://mlsanity.com/paper/2301.10611/) (2023)
- [Multi-target prediction for dummies using two-branch neural networks](https://mlsanity.com/paper/2104.09967/) (2021)
- [SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images](https://mlsanity.com/paper/2108.06810/) (2021)
- [Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification](https://mlsanity.com/paper/2208.02613/) (2022)
- [Domain Adaptation for Multi-label Image Classification: a Discriminator-free Approach](https://mlsanity.com/paper/2505.14333/) (2025)
- [Multi-Label Classification Framework for Hurricane Damage Assessment](https://mlsanity.com/paper/2507.02265/) (2025)
- [MCANet: A Multi-Scale Class-Specific Attention Network for Multi-Label Post-Hurricane Damage Assessment Using UAV Imagery](https://mlsanity.com/paper/2509.04757/) (2025)
- [Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2510.08269/) (2025)
- [MARC: Multi-Label Adaptive Retrieval Contrastive Loss for Remote Sensing Images](https://mlsanity.com/paper/2512.16294/) (2025)
- [Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2601.08446/) (2026)
- [Revisiting Aerial Scene Classification on the AID Benchmark](https://mlsanity.com/paper/2601.18263/) (2026)
- [HELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification](https://mlsanity.com/paper/2603.11783/) (2026)
- [MAPLE: Multi-Path Adaptive Propagation with Level-Aware Embeddings for Hierarchical Multi-Label Image Classification](https://mlsanity.com/paper/2603.29784/) (2026)
- [Label-Decoupled Style Augmentation for Domain Generalization in Multi-Label Remote Sensing Scene Classification](https://mlsanity.com/paper/2607.12704/) (2026)
- [Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose?](https://mlsanity.com/paper/2607.20284/) (2026)

## Abstract (arXiv)

Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an LSTM layer can be introduced to modeling such label dependencies in a chain propagation manner, the efficiency might be questioned when certain labels are improperly inferred. To address this, we propose a novel aerial image multi-label classification network, attention-aware label relational reasoning network. Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module, 2) an attentional region extraction module, and 3) a label relational inference module. To be more specific, the label-wise feature parcel learning module is designed for extracting high-level label-specific features. The attentional region extraction module aims at localizing discriminative regions in these features and yielding attentional label-specific features. The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. The proposed network is characterized by its capacities of extracting discriminative label-wise features in a proposal-free way and reasoning about label relations naturally and interpretably. In our experiments, we evaluate the proposed model on the UCM multi-label dataset and a newly produced dataset, AID multi-label dataset. Quantitative and qualitative results on these two datasets demonstrate the effectiveness of our model. To facilitate progress in the multi-label aerial image classification, the AID multi-label dataset will be made publicly available.