# Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classification

arXiv: https://arxiv.org/abs/1807.11245 · HTML: https://mlsanity.com/paper/1807.11245/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 17 of 17)

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- [Weight Initialization Techniques for Deep Learning Algorithms in Remote Sensing: Recent Trends and Future Perspectives](https://mlsanity.com/paper/2102.07004/) (2021)
- [MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images](https://mlsanity.com/paper/2104.02846/) (2021)
- [AiTLAS: Artificial Intelligence Toolbox for Earth Observation](https://mlsanity.com/paper/2201.08789/) (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)
- [Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification](https://mlsanity.com/paper/2301.10611/) (2023)
- [Weakly Supervised Instance Attention for Multisource Fine-Grained Object Recognition with an Application to Tree Species Classification](https://mlsanity.com/paper/2105.10983/) (2021)
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- [Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage](https://mlsanity.com/paper/2206.00614/) (2022)
- [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)
- [Adaptive Gradient Calibration for Single-Positive Multi-Label Learning in Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2510.08269/) (2025)
- [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)

## Abstract (arXiv)

Aerial image classification is of great significance in remote sensing community, and many researches have been conducted over the past few years. Among these studies, most of them focus on categorizing an image into one semantic label, while in the real world, an aerial image is often associated with multiple labels, e.g., multiple object-level labels in our case. Besides, a comprehensive picture of present objects in a given high resolution aerial image can provide more in-depth understanding of the studied region. For these reasons, aerial image multi-label classification has been attracting increasing attention. However, one common limitation shared by existing methods in the community is that the co-occurrence relationship of various classes, so called class dependency, is underexplored and leads to an inconsiderate decision. In this paper, we propose a novel end-to-end network, namely class-wise attention-based convolutional and bidirectional LSTM network (CA-Conv-BiLSTM), for this task. The proposed network consists of three indispensable components: 1) a feature extraction module, 2) a class attention learning layer, and 3) a bidirectional LSTM-based sub-network. Particularly, the feature extraction module is designed for extracting fine-grained semantic feature maps, while the class attention learning layer aims at capturing discriminative class-specific features. As the most important part, the bidirectional LSTM-based sub-network models the underlying class dependency in both directions and produce structured multiple object labels. Experimental results on UCM multi-label dataset and DFC15 multi-label dataset validate the effectiveness of our model quantitatively and qualitatively.