# Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network

arXiv: https://arxiv.org/abs/2206.13392 · HTML: https://mlsanity.com/paper/2206.13392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [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)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Learning from Between-class Examples for Deep Sound Recognition](https://mlsanity.com/paper/1711.10282/) (2017)
- [Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1805.07319/) (2018)
- [Multi-Granularity Canonical Appearance Pooling for Remote Sensing Scene Classification](https://mlsanity.com/paper/2004.04491/) (2020)
- [Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification](https://mlsanity.com/paper/1802.03518/) (2018)
- [An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification](https://mlsanity.com/paper/2112.09172/) (2021)
- [Deep Learning Frameworks Applied For Audio-Visual Scene Classification](https://mlsanity.com/paper/2106.06840/) (2021)

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

- [A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification](https://mlsanity.com/paper/2211.02820/) (2022)

## Abstract (arXiv)

The task of remote sensing image scene classification (RSISC), which aims at classifying remote sensing images into groups of semantic categories based on their contents, has taken the important role in a wide range of applications such as urban planning, natural hazards detection, environment monitoring,vegetation mapping, or geospatial object detection. During the past years, research community focusing on RSISC task has shown significant effort to publish diverse datasets as well as propose different approaches to deal with the RSISC challenges. Recently, almost proposed RSISC systems base on deep learning models which prove powerful and outperform traditional approaches using image processing and machine learning. In this paper, we also leverage the power of deep learning technology, evaluate a variety of deep neural network architectures, indicate main factors affecting the performance of a RSISC system. Given the comprehensive analysis, we propose a deep learning based framework for RSISC, which makes use of the transfer learning technique and multihead attention scheme. The proposed deep learning framework is evaluated on the benchmark NWPU-RESISC45 dataset and achieves the best classification accuracy of 94.7% which shows competitive to the state-of-the-art systems and potential for real-life applications.