K-means in Python 3 on Sentinel 2 data

18 months ago I wrote about unsupervised classification of randomly extracted point data from satellite data. I have been meaning to follow it up with showing how straightforward it is to use the cluster algorithms in Sklearn to classify Sentinel 2 data. I have made this blog into a Juypter Notebook which is available here. Read more about K-means in Python 3 on Sentinel 2 data[…]

leaflet

Combining OpenCV and leaflet for simple web mapping

I am always on the look out for an easy way to build simple web maps. Ideally I would like to perform OpenCV in the browser but I am not aware of that possibility at present. I work with computer vision and satellite imagery a great deal and have written several blogs on the subject. Read more about Combining OpenCV and leaflet for simple web mapping[…]

Fastest image reader? Four ways to open a Satellite image in Python

I wasn’t sure what to call this post… “The Fastest Way to Read a Satellite Image in Python”? “Using Magic Functions in Jupyter Notebooks”? “Four Ways to Read Images into NumPy Arrays”? There are several ways; I’ve not even looked at PIL here, to read your Satellite data into a NumPy array. After all, if Read more about Fastest image reader? Four ways to open a Satellite image in Python[…]

pan sharpening

Pan Sharpening Sentinel 2 with Planet data

Pan sharpening is the process of increasing the spatial resolution of an RGB (Red, Green, Blue) image. Both Landsat 8 and Landsat 7 have a 15m spatial resolution panchromatic band. The benefit of pan sharpening is clear; it allows the production of a significantly sharpened RGB image. There is plenty written about pan sharpening – Read more about Pan Sharpening Sentinel 2 with Planet data[…]

Interactive Image Segmentation part 3 – Automation

This is the 3rd part in a series on interactive image segmentation. In part 1 I looked at how thresholding an image of coins has the potential to help map circular fields in the desert. In part 2 I applied this watershed algorithm to satellite data and created an output shapefile. In part 3 I Read more about Interactive Image Segmentation part 3 – Automation[…]

Interactive Image Segmentation part 2

This is the second part of a blog series on mapping circular fields. In part one I talked about the challenges for mapping in Desert environments and about how thresholding and the Watershed Algorithm can be used to detect coins – this offers a potentially useful way to map circular fields. These are a challenge Read more about Interactive Image Segmentation part 2[…]

Interactive Image Segmentation part 1

A few weeks ago I saw this tweet from UrtheCast We wish you a happy #EarthDay with this stunning #DEIMOS1 view of crop circles in the desert in #SaudiArabia! #PrecisionAg #Geoanalytics pic.twitter.com/szcSptjHMS — UrtheCast (@UrtheCast) April 22, 2017 It is a stunning image, captured by Deimos 1, of crop circles in Saudi Arabia. I really Read more about Interactive Image Segmentation part 1[…]