# Multispectral Image Fusion with PSO Algorithm
[An adaptive multispectral image fusion using particle swarm optimization
](https://ieeexplore.ieee.org/abstract/document/7985325) is the paper for this MATLAB code.
Description
----------
This code provides the fusion of PANchromatic (PAN) and MultiSpectral (MS) images using the Particle Swarm Optimization (PSO) algorithm. The steps for fusion is as follows:
1) Loading the dataset from its path
2) Pre-processing steps including downsampling and normalization
3) Initialization of PSO algorithm
4) Obtaining the primitive detail map for each spectral band
5) Extracting the edge detectors for PAN and MS images
6) Fine-tuning the gains of edge detectors using PSO algorithm
Loss Function
--------------
For the purpose of optimizing the gains of edge detectors, the ERGAS metric is minimized. This metric is one of the widely used metrics for the objective evaluation of fusion results.
Usage
------------
First you need to specify the path of your dataset.
For example:
addpath QuickBird_Data
The Main_PSO.m is the main framework of the proposed fusion framework. The _**pre-processing**_ steps as well as the obtaining _**fusion outcome**_ is put into this M-file. The ERGAS_Index.m and ERGAS.m files are used for the purpose of optimization. The optimized gains of _**edge detectors**_ are computed as the output of of PSO algorithm.
To _run_ the code, in the _command window_ use this:
Main_PSO.m
Objective Evaluation
----------
For objective assessment of the fusion result, first add the path of objective metric. For example:
addpath Objective_Evaluation
Sample Output
-----------
The MS, PAN and pansharpened result of the QuickBird dataset from Sandarbans, Bangladesh.
![LRMS](https://user-images.githubusercontent.com/48659018/56171542-5284ec00-5fab-11e9-93fb-a973ba1e8014.png)
![PAN](https://user-images.githubusercontent.com/48659018/56171559-603a7180-5fab-11e9-8626-c1103ca22e6d.png)
![Pansharpened](https://user-images.githubusercontent.com/48659018/56171570-6892ac80-5fab-11e9-86be-8f86e797e974.png)
Reference
--------
If you find this code helpful, please cite this paper:
A. Azarang and H. Ghassemian, "An adaptive multispectral image fusion using particle swarm optimization," in Proc. Iranian Conf. Elec. Eng. (ICEE), May 2017, pp. 1708-1712.
Contact
--------
If you have any question regarding the paper, codes and so on, do not hesitate to contact me.
Arian Azarang azarang@utdallas.edu
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【图像融合】一种基于粒子群优化的自适应多光谱图像融合附matlab代码+运行结果.zip
共38个文件
m:25个
png:4个
jpg:3个
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【图像融合】一种基于粒子群优化的自适应多光谱图像融合附matlab代码+运行结果.zip (38个子文件)
【图像融合】一种基于粒子群优化的自适应多光谱图像融合附matlab代码+运行结果
运行结果1.jpg 91KB
ERGAS_Index.m 3KB
expEdge.m 209B
Main_PSO.m 9KB
3.png 444KB
ERGAS.m 1KB
说明.txt 273B
QuickBird_Data
MS.mat 208KB
PAN.mat 949KB
【图像融合】一种基于粒子群优化的自适应多光谱图像融合附matlab代码及论文.md 4KB
仿真咨询.png 350KB
更多代码关注我.png 114KB
Objective_Evaluation
CC.m 147B
odd_extension.m 762B
SAM.m 2KB
onion_mult2D.m 719B
D_lambda.m 3KB
ERGAS.m 1KB
onions_quality.m 2KB
Q.m 1KB
ssim.m 6KB
img_qi.m 4KB
onion_mult.m 616B
MTF_PAN.m 2KB
D_s.m 3KB
RASE.m 594B
QNR.m 2KB
q2n.m 3KB
MTF.m 3KB
uqi.m 170B
RMSE.m 133B
norm_blocco.m 147B
运行结果2.jpg 195KB
打开.png 114KB
README.md 3KB
impGradDes.m 1KB
IranianCEE.2017.7985325.pdf 2.25MB
运行结果3.jpg 164KB
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