This repository contains implementation of our GRSL paper titled as Two-Pass Bilateral Smooth Filtering for Remote Sensing Imagery. In this paper, we propose a two-pass bilateral filter, TP-based BF, and an adaptive control scheme of range kernels for noise-invariant edge-preserving image smoothing. Experimental results on four aerial-imagery benchmark datasets show that our TP-based BF outperforms the existing bilateral filters in terms of both feature-aware and gradient-aware measures.

Authors: Bo-Hao Chen, Hsiang-Yin Cheng, Yi-Syuan Tseng, and Jia-Li Yin

Paper: PDF

• MATLAB R2019a
• MATLAB R2017b

• Windows 10
• Ubuntu 16.04

Might work under others, but didn't get to test any other OSs just yet.

1. To build noise dataset, you'll also need following datasets, and put the data in ./data/img_ori/.

• SIPI-Aerials
• COWC
• Inria-Aerial
• DOTA

2. Run the following script to generate noise image, and results will be saved in: ./data/img_noise/.

$ git clone https://github.com/bigmms/chen_grsl21_tpbf.git
$ cd chen_grsl21_tpbf
$ matlab
>> demo_noise

3. Run the following script to generate ground truth image, and results will be saved in: ./data/img_gt/.

>> demo_BF

4. Structure of the generated data should be：

├── data
    ├──img_gt             #folder for storing ground truth images
    │  ├── 0001.png                
    │  ├── 0002.png 
    │  └── ...
    ├──img_noise          #folder for storing noise images
    │  ├── 0001.png
    │  ├── 0002.png
    │  └── ... 
    └──img_ori            #folder for storing original images
       ├── 0001.png
       ├── 0002.png
       └── ...

>> demo_TPBF

The test results will be saved in: ./img_output/

The TPBF code is licensed under CC BY-NC-SA 4.0. Commercial usage is not permitted. If you use this code in a scientific publication, please cite the following paper:

@ARTICLE{ChenGRSL2021,  
	author={Chen, Bo-Hao and Cheng, Hsiang-Yin and Tseng, Yi-Syuan and Yin, Jia-Li}, 
	journal={IEEE Geoscience and Remote Sensing Letters},   
	title={Two-Pass Bilateral Smooth Filtering for Remote Sensing Imagery},   
	year={2022},  
	volume={19},  
	number={},  
	pages={1-5},  
	doi={10.1109/LGRS.2020.3048488}}