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.

本文旨在探讨如何使用深度学习技术完成遥感图片情景分类任务。通过对不同深度神经网络预训练模型和多头注意力机制进行评估，提出了一种基于深度学习的架构，并在 NWPU-RESISC45 数据集上取得了 94.7% 的准确率。