# A Novel Multi-scale Attention Feature Extraction Block for Aerial Remote Sensing Image Classification

arXiv: https://arxiv.org/abs/2308.14076 · HTML: https://mlsanity.com/paper/2308.14076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification](https://mlsanity.com/paper/2305.00679/) (2023)

## Built on by (most cited first, 1 of 1)

- [Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques](https://mlsanity.com/paper/2503.02510/) (2025)

## Abstract (arXiv)

Classification of very high-resolution (VHR) aerial remote sensing (RS) images is a well-established research area in the remote sensing community as it provides valuable spatial information for decision-making. Existing works on VHR aerial RS image classification produce an excellent classification performance; nevertheless, they have a limited capability to well-represent VHR RS images having complex and small objects, thereby leading to performance instability. As such, we propose a novel plug-and-play multi-scale attention feature extraction block (MSAFEB) based on multi-scale convolution at two levels with skip connection, producing discriminative/salient information at a deeper/finer level. The experimental study on two benchmark VHR aerial RS image datasets (AID and NWPU) demonstrates that our proposal achieves a stable/consistent performance (minimum standard deviation of 0.002) and competent overall classification performance (AID: 95.85% and NWPU: 94.09%).