Package: pyrsgis
Python-for-Remote-Sensing-and-GISpyrsgisenables the user to read, process and export GeoTIFFs. The module is built on the GDAL library but is much more convenient when it comes to reading and exporting GeoTIFs. There are several other functions available in this package that ease raster pre-processing. Please check thedocumentation pagefor a list of functions that pyrsgis offers along with the sample code.Feedback and doubts from users are most welcome. Since this is an open-source volunatry project, I always look forward to contributors. You can write to me atpratkrt@gmail.com.pyrsgis is available on both, PyPI and Anaconda. Please check theinstallation pagefor details.Recommended citation:Tripathy, P. pyrsgis: A Python package for remote sensing and GIS data processing. V0.4. Available athttps://github.com/PratyushTripathy/pyrsgis.Sample code (click to expand)1. Reading .tif extension fileImport the module and define the input file path.frompyrsgisimportrasterfile_path=r'D:/your_file_name.tif'To read all the bands of a stacked satellite image:ds,arr=raster.read(file_path,bands='all')where,dsis the data source similar to GDAL andarris the numpy array that contains all the bands of the input raster. Thearrcan be 2D or 3D depending on the input data. One can check the shape of the array using theprint(arr.shape)command. Thebandsargument in theraster.readfunction defaults to'all'.To read a list of bands of a stacked satellite image:ds,arr=raster.read(file_path,bands=[2,3,4])Passing the band numbers in a list returns bands 2, 3 & 4 as three-dimensional numpy array.To read a specific band from stacked satellite image:ds,arr=raster.read(file_path,bands=2)Passing a single band number returns that particular band as two-dimensional numpy array.To read a single band TIF file:ds,arr=raster.read(file_path)Since thebandsargument defaults to'all', this will read all the bands in the input file, here, one band only.2. Exporting .tif extension fileIn all the below examples, it is assumed that the number of rows and columns, and the cell size of the input and output rasters are the same. All these are stored in the `ds` variable, please see details here: link.To export all bands of a 3D array:out_file_path=r'D:/sample_file_all_bands.tif'raster.export(arr,ds,out_file_path,dtype='int',bands='all')Thedtypeargument in the above function by default is set to'default', which is'int'--16-bit integer. If the data type in the provideddsis notintand the paramter is set todefault, then the data type of thedswill be used. If there is a disagreement and thedtypeargument is explicitly specified, it will override the data type ofds. Please be careful to change this while exporting arrays with large values. Similarly, to export float type array (eg. NDVI), usedtype = 'float'. Data type of high pixel-depth, e.g. Integer32, Integer64, or float type uses more space on hard drive, so the default has been set to integer. To export any float datatype, the argument should be passed explicitly.These are the options for thedtypeargument:byte,cfloat32,cfloat64,cint16,cint32,float32,float64,int16,int32,uint8,uint16,uint32.The NoData value can be explicitly defined using thenodataparameter, this defaults to-9999.To export a list of bands of a 3D array:out_file_path=r'D:/sample_file_bands_234.tif'raster.export(arr,ds,out_file_path,bands=[2,3,4])To export any one band of a 3D array:out_file_path=r'D:/sample_file_band_3.tif'raster.export(arr,ds,out_file_path,bands=3)To export a single band array:out_file_path=r'D:/sample_file.tif'raster.export(arr,ds,out_file_path)where,arrshould be a 2D array.Export the raster with compression:Compression type can also be defined while exporting the raster by using thecompressparameter.LZW.DEFLATE, etc. are a couple of options. Defaults toNone.Example with all default parameters:out_file_path=r'D:/sample_file.tif'raster.export(band,ds,filename='pyrsgis_outFile.tif',dtype='int',bands=1,nodata=-9999,compress=None)3. Converting TIF to CSVGeoTIFF files can be converted to CSV files using *pyrsgis*. Every band is flattened to a single-dimensional array, and converted to CSV. These are very useful for statistical analysis.Import the function:frompyrsgis.convertimportrastertocsvTo convert all the bands present in a folder:your_dir=r"D:/your_raster_directory"out_file_path=r"D:/yourFilename.csv"rastertocsv(your_dir,filename=out_file_path)Generally the NoData or NULL values in the raster become random negative values, negatives can be removed using thenegativeargument:rastertocsv(your_dir,filename=out_file_path,negative=False)At times the NoData or NULL values in raster become '127' or '65536', they can also be removed by declaring explicitly.rastertocsv(your_dir,filename=out_file_path,remove=[127,65536])This is a trial and check process, please check the generated CSV file for such issues and handle as required.Similarly, there are bad rows in the CSV file, representing zero value in all the bands. This takes a lot of unnecessary space on drive, it can be eliminated using:rastertocsv(your_dir,filename=out_file_path,badrows=False)4. Creating northing and easting using a reference rasterpyrsgis allows to quickly create the northing and easting rasters using a reference raster, as shown below:The cell value in the generated rasters are the row and column number of the cell. To generate these GeoTIFF files, start by importing the function:frompyrsgis.rasterimportnorthing,eastingreference_file_path=r'D:/your_reference_raster.tif'northing(reference_file_path,outFile=r'D:/pyrsgis_northing.tif',flip=True)easting(reference_file_path,outFile=r'D:/pyrsgis_easting.tif',flip=False)As the name suggests, theflipargument flips the resulting rasters.Thevalueargument defaults tonumber. It can be changed tonormalisedto get a normalised layer. The other option forvalueargument iscoordinates, which produces the raster layer with cell centroids. Please note that if thevalueargument is set tonormalised, it will automatically adjust the flip value, i.e. False, both in easting and northing functions. Similarly, thedtypeparameter auto-adjusts with the data type, but can be changed to a higher pixel depth whenvalueargument isnumber. Example with all parameters:northing(reference_file_path,outFile='pyrsgis_northing.tif',flip=True,value='number',dtype='int16')easting(reference_file_path,outFile='pyrsgis_easting.tif',flip=False,value='number',dtype='int16')5. Shifting raster layersYou can shift the raster layers using either the 'shift' or 'shift_file' function. The 'shift' function allows to shift the raster in the backend, whereas, the 'shift_file' directly shifts the GeoTIF file and stores another file.To shift in the backend:frompyrsgisimportraster# Define the path to the input file and get the data sourceinfile=r"D:/path_to_your_file/input.tif"ds,arr=raster.read(infile)# Define the amount of shift requireddelta_x=15delta_y=11.7# shift the rastershifted_ds=raster.shift(ds,x=delta_x,y=delta_y,shift_type)# if you wish to exportraster.export(arr,ds,out_file,dtype='int',bands=1,nodata=-9999)Here, 'ds' is the data source object that is created when the raster is read using 'raster.read' command. 'x' and 'y' are the distance for shifting the raster. The 'shift_type' command let's you move the raster either by the raster units or number of cells, the valid options are 'unit' and 'cell'. By default, the 'shift_type' is 'unit'.To shift a GeoTIFF file:frompyrsgisimportraster# Define the path to the input and output fileinfile=r"D:/path_to_your_file/input.tif"outfile=r"D:/path_to_your_file/shifted_output.tif"# Define the amount of shift requireddelta_x=15delta_y=11.7# shift the rasterraster.shift_file(infile,x=delta_x,y=delta_y,outfile=outfile,shift_type='unit',dtype='uint16')Most of the parameters are same as the 'shift' function. The 'dtype' parameter is same as used in the 'raster.export' function.6. Create image chips for Convolutional Neural Network (CNN)CNNs require image chips for training and prediction. Remote sensing images are large sized two or three-dimesional images, this module enables creating image chips directly from TIF files or arrays. The input data and size of image chips are required.To create image chips from array:frompyrsgisimportrasterfrompyrsgis.mlimportimageChipsFromArray# read the TIF file(s) (both are of different sizes - for demonstration)single_band_file=r'path/to/single_band.tif'multi_band_file=r'path/to/multi_band.tif'# this is a Landsat 5 TM image (7 bands stacked)# read the files as array using pyrsgis raster.read module_,single_band_array=raster.read(single_band_file)_,multi_band_array=raster.read(multi_band_file)# create image chipssingle_band_chips=imageChipsFromArray(single_band_array,x_size=5,y_size=5))multi_band_chips=imageChipsFromArray(multi_band_array,x_size=5,y_size=5))print(single_band_chips.shape)print(multi_band_chips.shape)The output:(91125, 5, 5)
(987552, 5, 5, 7)Image chips can also be generated directly from TIF files using following:frompyrsgis.mlimportimageChipsFromFile# read the TIF file(s) (both are of different sizes - for demonstration)single_band_file=r'path/to/single_band.tif'multi_band_file=r'path/to/multi_band.tif'# this is a Landsat 5 TM image (7 bands stacked)# create image chipssingle_band_chips=imageChipsFromFile(single_band_file,x_size=5,y_size=5))multi_band_chips=imageChipsFromFile(multi_band_file,x_size=5,y_size=5))print(single_band_chips.shape)print(multi_band_chips.shape)This will result in the same output as the one above.7. Reading directly from .tar.gz files (beta)Currently, only Landsat data is supported.importpyrsgisfile_path=r'D:/your_file_name.tar.gz'your_data=pyrsgis.readtar(file_path)The above code reads the data and stores in theyour_datavariable.Various properties of the raster can be assessed using the following code:print(your_data.rows)print(your_data.cols)This will display the number of rows and columns of the input data.Similarly, the number of bands can be checked using:print(your_data.nbands)On reading the .tar.gz files directly, pyrsgis determines the satellite sensor. This can be checked using:print(your_data.satellite)This will display the satellite sensor, for instance, Landsat-5, Landsat-8, etc.If the above code shows the correct satellite sensor, then the list of band names of the sensor (in order) can easily be checked using:print(your_data.bandIndex)Any particular band can be extarcted using:band_number=1your_band=your_data.getband(band_number)The above code returns the band as array which can be visualised using:display(your_band,maptitle='Title of your image',cmap='PRGn')or, directly using:band_number=1display(your_data.getband(band_number),maptitle='Title of your image',cmap='PRGn')The generated map can directly be saved as an image.The extracted band can be exported using:out_file_path=r'D:/sample_output.tif'your_data.export(your_band,out_file_path)This saves the extracted band to the same directory.To export the float type raster, please define thedatatypeexplicitly, default is 'int':your_data.export(your_band,out_file_path,datatype='float')The NDVI (Normalised Difference Vegetaton Index) can be computed easily.your_ndvi=your_data.ndvi()Normalised difference index between any two bands can be computed using:norm_diff=your_data.nordif(bandNumber2,bandNumber1)This computes (band2-band1)/(band2+band1) in the back end and returns a numpy array. The resulting array can be exported using:out_file_path=r'D:/your_ndvi.tif'your_data.export(your_ndvi,out_file_path,datatype='float')Be careful with the float type of NDVI.