danclewley

#!/usr/bin/env python

"""
Create a single layer tif in WGS84 projection from
NEODAAS (https://www.neodaas.ac.uk/) netCDF files in mercator projection.

Converts the location array to GCPs and uses these to warp the image with
GDAL.

Dan Clewley (dac@pml.ac.uk).

danclewley

<!-- SPDLib Metrics file -->
 
<spdlib:metrics xmlns:spdlib="http://www.spdlib.org/xml/">
 
<!-- HOME -->
 
<spdlib:metric metric="home" field="HOME"/>
 
<!-- WD -->
 

danclewley

"""
Example of reading the spectra from a single pixel using
arsf_envi_reader (https://github.com/pmlrsg/arsf_tools)

Dan Clewley, NERC-ARF-DAN
2018-05-01
"""
from arsf_envi_reader import numpy_bin_reader
from arsf_envi_reader import envi_header
from matplotlib import pyplot as plt

danclewley

#!/usr/bin/env python
"""
Apply RSGISLib segmentation to image
using standard or tiled approach.

For more details see:

https://spectraldifferences.wordpress.com/2016/07/09/scalable-image-segmentation-using-rsgislib/

Dan Clewley (09/07/2016)

danclewley

#!/usr/bin/env python
"""
Apply Savitzky-Golay filter to an ENVI BIL file

Uses scipy

http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html

And arsf_envi_reader (available from: https://github.com/pmlrsg/arsf_tools)

danclewley

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Mosaic files within a zip archive using RSGISLib

Author: Dan Clewley
Creation Date: 02/09/2015

Designed to mosaic Environemnt Agency LiDAR-derived DTM and DSM files
downloaded in ASCII format from http://environment.data.gov.uk/ds/survey

danclewley

#!/usr/bin/env python
"""
A script to create a GIF from a sequence of images using
ImageMagick.
Applies natural sorting to file names.
Dan Clewley (http://spectraldifferences.wordpress.com)
"""

import argparse
import re

danclewley

import sys
from rios import applier
from rios import cuiprogress
import numpy

def calcstats(info, inputs, outputs):
    # Calc standard deviation of bands for each pixel
    stdev_pixels = numpy.std(inputs.inimage, axis=0)
    # Reshape to make 3-dimensional array (required for output)
    outputs.outimage = stdev_pixels.reshape((1,stdev_pixels.shape[0],stdev_pixels.shape[1]))

danclewley

import sys
from rios import applier
from rios import cuiprogress
import numpy

def calcstats(info, inputs, outputs):
    # Calc standard deviation of bands for each pixel
    stdev_pixels = numpy.std(inputs.inimage, axis=0)
    # Reshape to make 3-dimensional array (required for output)
    outputs.outimage = stdev_pixels.reshape((1,stdev_pixels.shape[0],stdev_pixels.shape[1]))

danclewley

#!/usr/bin/env python
#
# A script topographically correct SAR data using RIOS applier.
#
# Dan Clewley (clewley@usc.edu) - 05/02/2013
#
# The correction uses:
#
# sigma0_norm = sigma0 * (A_flat / A_slope) * (cos(theta_ref) / cos(theta_loc))^n
#