Extracting the central strip from LANDSAT 7 imagery

Extracting the central strip from LANDSAT 7 imagery

February 8, 2016

Here is a simple Python code to extract the central strip from Landsat 7 imagery (SLC-off),  that is not affected by the SLC failure. The algorithm shrinks the striping zones through a morphological filter (erosion) and creates a new shapefile AOI that extracts the desired raster extent without striping effects. The code is based on Python for ArcGIS (arcpy) – so you require a ArcGIS license.

General steps:

  1. Loop through all Landsat 7 data folders
  2. Stack bands for each image
  3. Create a mask
  4. Erode the mask by 20 pixels
  5. Convert the mask to polygon
  6. Create a minimum bounding box
  7. Clip the original raster through the bbox

 

import arcpy
from arcpy.sa import *

import sys,os

#  Environment settings (Activate Spatial Analyst, Overwrite Outputs allowed and TIFF compression is LZW)
arcpy.CheckOutExtension("spatial")
arcpy.env.overwriteOutput = True
arcpy.env.compression = 'LZW'

# this is your main directory with all unzipped Landsat datasets
 rootdir = "D:\\DATA\\Landsat7\\"

# create scratch folder "temp" 
temp = "D:\\DATA\\temp\\"

# loop through directory with all unzipped Landsat 7 folders
 for files in os.listdir(rootdir):   
    files = os.path.join(rootdir, files)   
    
    # for each loop the subdir "files" is now the current workspace 
    # (e.g. LE71520322015157-SC20160224113319) that contains the Landsat bands
    arcpy.env.workspace = files  
    rasters = arcpy.ListRasters("*", "TIF")  
    
    # create empty array
    stack_liste = []  
    # loop through all rasters in subdir
    for raster in rasters:   

        image = arcpy.Raster(raster) 
        name  = image.name 
        index = name.split("_")[0]  

        # fill up the array only with the actual spectral bands        
        sr = "_sr_band"  
        if sr in raster:   
            stack_liste.append(raster)             

    # now stack all bands within the array
    stack_name = files + "\\" + index + "_stack.tif"    
    arcpy.CompositeBands_management(stack_liste, stack_name)  

    # convert the image stack to a mask by logical operation with an absurd value that will result in an output "0"
    con = EqualTo(stack_name, 123456789)  

    # now shrink the raster mask with value "0" by 20 pixels
    shrink = temp + "shrink"  
    shrinking = Shrink(con, 20, 0) 
    shrinking.save(shrink)  

    zone = temp + "zone.shp" 
    bbox = temp + "bbox.shp"  

    # conver the shrunk mask to polygon and create a minimum bounding box
    arcpy.RasterToPolygon_conversion(shrink, zone, "NO_SIMPLIFY", "VALUE") 
    arcpy.MinimumBoundingGeometry_management(zone, bbox, "RECTANGLE_BY_WIDTH", "NONE")  

    # now use that bounding box as a mask to cut out the central nadir strip from the original stack
    # Final result 
    extract = files + "\\" + index + "_aoi.tif"  
    ExtractByMask = arcpy.sa.ExtractByMask(stack_name, bbox) 
    ExtractByMask.save(extract)

 

you may also like:

Poster Presentation at AK Hydrologie, Bonn

Poster Presentation at AK Hydrologie, Bonn

From November 13 to 15, Sofia Haag and Christian Schäfer attended the AK Hydrologie workshop in Bonn, where they presented their work from the EO4CAM project. The first day featured an insightful field excursion to the Ahrtal region, led by Prof. Dr. Jürgen Herget,...

A Glimpse into Our Research: Data on Display in the Foyer

A Glimpse into Our Research: Data on Display in the Foyer

Stepping into the foyer, visitors are now greeted by large, striking images that tell the story of our research through data. Each visual represents a unique scientific perspective – from the Arctic to the cultivated landscapes of Bavaria, and from forest canopies to...

Successful MSc defense by Sonja Maas

Successful MSc defense by Sonja Maas

Big congratulations to Sonja Maas, who successfully defended her Master thesis today on the highly relevant and increasingly pressing topic: LiDAR-Based Acquisition Strategies for Forest Management Planning in a Mature Beech Stand Supervised by Dr. Julian Fäth and...

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Visit at the Institute for Geoinformatics (IFGI) at University of Münster

Two days ago, our PostDoc Dr. Jakob Schwalb-Willmann visited the Institute for Geoinformatics at University of Münster to give a talk at IFGI’s GI Forum titled “Can animals be used to classify land use? Employing movement-tracked animals as environmental informants using deep learning”.