# A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification

arXiv: https://arxiv.org/abs/2106.07879 · HTML: https://mlsanity.com/paper/2106.07879/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [Understanding Batch Normalization](https://mlsanity.com/paper/1806.02375/) (2018)
- [Multiple instance dense connected convolution neural network for aerial image scene classification](https://mlsanity.com/paper/1908.08156/) (2019)
- [Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification](https://mlsanity.com/paper/1706.01171/) (2017)
- [DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories](https://mlsanity.com/paper/2002.06832/) (2020)

## Built on by (most cited first, 5 of 5)

- [Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model](https://mlsanity.com/paper/2208.03987/) (2022)
- [LSKNet: A Foundation Lightweight Backbone for Remote Sensing](https://mlsanity.com/paper/2403.11735/) (2024)
- [A Multi-Stage Duplex Fusion ConvNet for Aerial Scene Classification](https://mlsanity.com/paper/2203.16325/) (2022)
- [MetaMorphosis: Task-oriented Privacy Cognizant Feature Generation for Multi-task Learning](https://mlsanity.com/paper/2305.07815/) (2023)
- [Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques](https://mlsanity.com/paper/2503.02510/) (2025)

## Abstract (arXiv)

In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem. The core of our technique is a layer selection strategy, named ReLU-Based Feature Fusion (RBFF), that extracts feature maps from a pretrained CNN-based single-object image classification model, namely MobileNetV2, and constructs a model for the aerial scene classification task. RBFF stacks features extracted from the batch normalization layer of a few selected blocks of MobileNetV2, where the candidate blocks are selected based on the characteristics of the ReLU activation layers present in those blocks. The feature vector is then compressed into a low-dimensional feature space using dimension reduction algorithms on which we train a low-cost SVM classifier for the classification of the aerial images. We validate our choice of selected features based on the significance of the extracted features with respect to our classification pipeline. RBFF remarkably does not involve any training of the base CNN model except for a few parameters for the classifier, which makes the technique very cost-effective for practical deployments. The constructed model despite being lightweight outperforms several recently proposed models in terms of accuracy for a number of aerial scene datasets.