# Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning
This repository contains code of the paper abstract `Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning` submitted to IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2023. This work has been done at the [Remote Sensing Image Analysis group](https://www.rsim.tu-berlin.de/menue/remote_sensing_image_analysis_group/) by [Baris Buyuktas](https://rsim.berlin/team/members/baris-buyuktas/), [Gencer Sumbul](https://rsim.berlin/team/members/gencer-sumbul/), and [Begüm Demir](https://begumdemir.com/).
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## Introduction
Remote sensing (RS) images are usually stored in compressed format to reduce the storage size of the archives. Thus, existing content-based image retrieval (CBIR) systems in RS require decoding images before applying CBIR (which is computationally demanding in the case of large-scale CBIR problems). To address this problem, in this paper, we present a joint framework that simultaneously learns RS image compression and indexing, eliminating the need for decoding RS images before applying CBIR. The proposed framework is made up of two modules. The first module aims at effectively compressing RS images. It is achieved based on an auto-encoder architecture. The second module aims at producing hash codes with a high discrimination capability. It is achieved based on a deep hashing method that exploits soft pairwise, bit-balancing and classification loss functions. We also propose a two stage learning strategy with gradient manipulation techniques to obtain image representations that are compatible with both RS image indexing and compression.
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## Prerequisites
The code in this repository requires Python 3.7.6, pytorch 1.7.0 and ranger coder.
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***
# Editing this README
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## Datasets
* [BigEarthNet](http://bigearth.net)
## Name
Choose a self-explaining name for your project.
## Description
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## Installation
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## Usage
Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
Downloaded data should be placed in a folder named `Dataset` and keep the original structure as following:
```
Dataset
└───BigEarthNet
| └───S2A_MSIL2A_20170613T101031_0_48
| │ S2A_MSIL2A_20170613T101031_0_48_B0
| │ ...
| ...
|
└───...
```
> **Note**:
> The train/val/test splits of the dataset and its subsets are placed in ./hashing-and-compression/datasets.
>
> To load the data from `memory-map` files for fast processing, set the flag `--flag_npmem` to create and load binary files.
>
> To use the subset data, set the flag `--flag_subset`.
### Logging results with W&B
* Create an account here (free): https://wandb.ai
* After the account is set, make sure to include your API key in `parameters.py` under `--wandb_key`.
* Set the flag `--log_online` to use wandb logging, if the network is unavailable in the training environment, set the flag `--wandb_dryrun` to make wandb store the data locally, and upload the data with the command `wandb sync <$path/wandb/offline..>`
* Set `--project`, `--group` and `--savename` during the training.
## Training
The training of the joint model is divided into two stages, the first stage is done by `train_compression.py`, and the second stage is done by `train.py`.
The training of the hashing baseline is done by `train_hashing.py`.
All the parameters are listed and explained in `parameters.py`.
A set of exemplary runs is provided in `./hashing-and-compression/sample_run`.
If the training is stopped accidentally, it can be resumed by setting `--load_from_checkpoint`, load the check point from `$save_path/every_epoch.pth.tar`.
### Common setups
The following are common setups for all the training scripts:
* `--source_path`: Path for the dataset, eg. `$path/Dataset`.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
## Authors
**Jun Xiang**
xj.junxiang@gmail.com
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
**Gencer Sümbül**
http://www.user.tu-berlin.de/gencersumbul/
## Authors and acknowledgment
Show your appreciation to those who have contributed to the project.
**Nimisha Thekke Madam**
nimiviswants@gmail.com
## License
For open source projects, say how it is licensed.
The code in this repository is licensed under the **MIT License**:
## Project status
If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
```
MIT License
Copyright (c) 2022 The Authors of The Paper, "A Novel Framework to Jointly Compress and Index Remote Sensing Images for Efficient Content-based Retrieval"
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
