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

TL;DR

This paper presents a deep learning framework using transfer learning and multihead attention for remote sensing image classification, achieving high accuracy on a benchmark dataset and demonstrating potential for practical applications.

Contribution

It introduces a novel deep learning framework combining transfer learning and multihead attention for improved remote sensing image scene classification.

Findings

Achieved 94.7% accuracy on NWPU-RESISC45 dataset.

Outperforms many existing methods in classification accuracy.

Demonstrates the effectiveness of attention mechanisms in RSISC.

Abstract

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.