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

• URL: http://arxiv.org/abs/2206.13392v1
• Date: Mon, 20 Jun 2022 10:05:38 GMT
• Title: Remote Sensing Image Classification using Transfer Learning and Attention Based Deep Neural Network
• Authors: Lam Pham, Khoa Tran, Dat Ngo, Jasmin Lampert, Alexander Schindler
• Abstract summary: 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%.
• Score: 59.86658316440461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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