Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'
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<strong>Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'</strong> In this repository, we provide the implementation of the algorithms developed in the paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval' along with the experimental results.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The licences valid for the elements of this repository are discussed under point "2. Licenses" below. <em><strong>1. Structure</strong></em> The repository contains the following items: "data" - the results from our experiments "lib" - some external functions used in the experiments "make_data" - the training and test data "fmt-vgg.py" - the FMT-RAN model "stn.py" - the STN module of ST-RAN "st_ran.py" - the ST-RAN model "README" - this text here. "LICENSE" - the MIT License <strong><em>2. License</em></strong> The following licenses apply for the files and folders: The files "stn.py" and "spatial_transformer_tutorial.py" in the folder "lib" are from the GitHub repository https://github.com/GHamrouni/stn-tuto and therefore are under the copyright of its repository owner Ghassen Hamrouni. All other files are under the MIT License. The MIT License is included here as file "LICENSE". <em><strong>3. Contact</strong></em> 1. Dr. Zhize WU, wuzz@hfuu.edu.cn<br> 2. Dr. Thomas WEISE, tweise@hfuu.edu.cn, tweise@ustc.edu.cn Institute of Applied Optimization, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China
**论文《面向遥感图像检索(Remote Sensing Image Retrieval)的旋转感知表征学习(Rotation-Aware Representation Learning)》配套实验数据** 本仓库提供了论文《面向遥感图像检索(Remote Sensing Image Retrieval)的旋转感知表征学习(Rotation-Aware Representation Learning)》中所提出算法的实现代码，以及对应的实验结果。本仓库旨在提供可用于验证、复现本研究工作的全部要素，以及可复现我们研究结论的全套工具。本仓库内所有内容所适用的许可协议将在下文第2节「许可协议」中予以说明。 ### 1. 仓库结构 本仓库包含以下内容： - `data`：本研究的实验结果 - `lib`：实验中使用的部分外部工具函数 - `make_data`：训练与测试数据集 - `fmt-vgg.py`：FMT-RAN模型实现代码 - `stn.py`：ST-RAN模型的STN(Spatial Transformer Network)模块 - `st_ran.py`：ST-RAN模型实现代码 - `README`：本说明文档 - `LICENSE`：MIT许可协议(MIT License) ### 2. 许可协议 本仓库内的文件与文件夹适用以下许可规则： 位于`lib`文件夹下的`stn.py`与`spatial_transformer_tutorial.py`源自GitHub仓库https://github.com/GHamrouni/stn-tuto，因此其版权归该仓库所有者Ghassen Hamrouni所有。其余所有文件均适用MIT许可协议(MIT License)，本协议以`LICENSE`文件形式附于本仓库中。 ### 3. 联系方式 1. 吴智泽博士，邮箱：wuzz@hfuu.edu.cn 2. 托马斯·魏斯博士，邮箱：tweise@hfuu.edu.cn、tweise@ustc.edu.cn 中国安徽省合肥市蜀山区合肥经济技术开发区锦绣大道99号合肥大学南区2校区人工智能与大数据学院应用优化研究所，邮编：230601
