celoyd

#!/usr/bin/env python

# ndvi.py red.tif nir.tif output-ndvi.tif
# Calculate NDVI (see Wikipedia). Assumes atmospheric correction.
# (Although I use it without all the time for quick experiments.)

import numpy as np
from sys import argv
from osgeo import gdal, gdalconst

celoyd

Himawari-8 animation tutorial

Here’s how to make animations like this one. It requires intermediate Unix command-line knowledge, to install some tools and to debug if they don’t work. You’ll need these utilities:

• curl (or you can translate to wget)
• convert and montage, part of ImageMagick
• ffmpeg, plus whatever codecs
• parallel, for iteration that’s nicer than shell for loops or xargs
• run everything in zsh for leading 0s in numerical ranges to work

celoyd

celoyd

Learning to despeckle sar with speckly targets

These are notes from a one-day project to test a hunch. The idea is to train a convolutional neural network to remove speckle from sar (synthetic aperture radar) using only one other observation – with its own speckles – as the target. This method does not come close to state of the art despeckling, and can be biased by the skewed distribution of noise in a way that makes it useless for quantitative research. However, I hadn’t noticed it in the literature and I think it’s kind of funny, so I’m writing it up.

Everything here is about Sentinel-1 L1 GRD-HD data, since it’s what I used, since it’s free.

Speckle

Sar observations contain speckle, a form of interference related to the sparkles in reflected laser light. By some definitions speckle is not noise, since it’s physically real outside the sensor and contains information, but we will treat it as noise. Speckle is (close enough to) independent between radar chirps, a.k.a. looks, and even its distributio

celoyd

celoyd

#!/usr/bin/env python

# Demo a brightened luminance-only inversion on 8-bit images

from skimage import io
import numpy as np
from sys import argv


# RGB <-> YCoCg-R almost straight from Wikipedia

celoyd

#!/usr/bin/env zsh

# ./day.sh month_number morning_day evening_day zoom_level

# Strictly alpha: for example, does not know how to handle
# month edges or unscheduled missing images.

set -eu

year=$1

celoyd

from skimage import io, filters
import numpy as np
import sys

# ./hi8-deband.py input.png output.png
# Cherrypicked sample before/after: http://basecase.org/2016/1/hi8corr

'''
Himawari-8 has slight noise along scanlines. It seems basically uncorrelated
between rows, and varies smoothly on the scale of about 100 columns.

celoyd

Free Idea: Enhancing Astronaut Photography of Earth

Beta version. May contain bad ideas.

By a free idea I mean something that I think is probably fun and probably possible but that I don’t have the combination of time, skill, energy, patience, etc. to do myself. I hope someone does this. I hope someone reads this and does just the specific part that they’re interested in. I’m trying to get the idea out there without giving the impression that it’s my project. It’s just an idea.

To do the whole thing as laid out here I think you’d need at least an intermediate understanding of convolutional neural networks for image processing, access to a GPU, some sense of geography and astronomy (to gut-check your intermediate results), and a reasonable internet connection to download the images.

The idea

celoyd

import himawari.HimawariScene as hsd
from sys import argv
import rasterio as rio
import numpy as np

scaleup = 64

scene = hsd.HimawariScene(argv[1])

rad = scene.radiances()