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https://github.com/JavierLopatin/Python-Remote-Sensing-Scripts

Python 3.X scripts for remote sensing processing
https://github.com/JavierLopatin/Python-Remote-Sensing-Scripts

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Python 3.X scripts for remote sensing processing

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README 

        

# Python-Remote-Sensing-Scripts



## A set of python scripts for remote sensing processing. All functions were created using Python 3.6



### As an example, I will use a UAV-based hyperspectral image of a peatland in central-south Chile. The image have 41 bands (10 nm width) ranging form 480-880 nm and a pixel size of 10 cm. The green dots correspond to plots where measurements of biomass, species composition and carbon stock information were taken:



![alt text](/README/peatland.PNG)



### With our scripts you can first extract the spectra in the plots location in a few seconds



```terminal

python ExtractValues.py -r peatland.tif -s plots.shp -i ID

```



### Check out the output






  

    

      

      N

      B1

      B2

      B3

      B4

      B5

      B6

      B7

      B8

      B9

      ...

      B32

      B33

      B34

      B35

      B36

      B37

      B38

      B39

      B40

      B41

    

  

  

    

      0

      1.0

      1509.813451

      2291.564899

      3109.637130

      3962.194325

      4674.092289

      5177.553225

      5526.183572

      5698.716701

      5730.444486

      ...

      15122.021006

      15696.353105

      15488.906034

      15259.373692

      15370.818661

      15988.557692

      16801.212295

      16728.998597

      16846.117603

      17324.593108

    

    

      1

      2.0

      1708.011608

      2617.267914

      3579.341584

      4624.986406

      5542.596761

      6221.865500

      6708.542956

      6970.212103

      7036.433086

      ...

      20393.572983

      20991.396063

      20751.881674

      20494.922936

      20547.978802

      21333.448745

      21717.872021

      21661.447804

      21420.735874

      21518.602349

    

    

      2

      3.0

      12.875854

      211.439792

      1201.264700

      2444.936965

      3603.781002

      4468.655196

      5054.639060

      5373.096807

      5479.100261

      ...

      40287.680262

      40283.248111

      39078.153197

      37895.060076

      38651.953385

      39584.352791

      40869.073804

      39726.095768

      39378.668239

      37695.083542

    

    

      3

      4.0

      214.950019

      852.952500

      1827.763722

      2946.394303

      3962.429581

      4700.627478

      5189.396823

      5428.162923

      5479.461922

      ...

      16865.061517

      17382.712572

      17022.812516

      16595.132046

      16618.242789

      17052.205065

      17653.929336

      17440.521948

      17410.552106

      17704.214614

    

    

      4

      5.0

      2704.663992

      3474.524823

      4291.852040

      5139.117787

      5834.414719

      6324.879534

      6673.001772

      6841.418896

      6859.041616

      ...

      19038.206233

      19714.628088

      19361.587963

      19008.547838

      19256.278283

      20024.457928

      20623.883552

      20655.435636

      20865.324231

      20861.972693

    

  






 ### You can also perform a MNF transformation of the data. This function have several options, like applying Savitzky Golay filtering and brightness normalization of the spectra. The basic function is like (image resample to 2m in the example):



```terminal

python MNF.py -i peatland.tif

```



![alt text](/README/MNF.png)



### Get the Gray-Level Co-Occurrence Matrix (GLCM) textures from an image. Here, we used the first MNF component as raster imput with a moving window of 5 X 5 pixels (default):



```terminal

python GLCM.py -i peatland_MNF.tif

```



![alt text](/README/GLCM.png)



### Finally, we can also obtain texture information from point clouds (in this case based in the UAV photogrametric point cloud) based on the Canupo algorithm proposed by [This paper](https://www.sciencedirect.com/science/article/pii/S0924271612000330), which is also implemented in the [CloudCompare](http://www.danielgm.net/cc/) LiDAR software. Nevertheless, both the paper and the software implement the transformation to generate poin-based classification while this python script produces texture rasters to be use in any application:



```terminal

python canupo.py -i lidar.txt -s 1 5 1 -r 1

# scales: 1,2,3,4,5 m; output resolution 1 m

```



![alt text](/README/canupo.png)