There are so many ways to download Sentinel 2 data. I am not going to list them all here, but most involve an interaction with a website that contains a map. This is fine for many users. Sometimes though it is preferable to access via the command line or a script and again there are… Read More »sentinelsat – using Python to search and download 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 »K-means in Python 3 on Sentinel 2 data
There are a variety of ways to download Sentinel 2 data, for example here, here or here. One of the easiest ways, and it seems to at least to me the fastest, is through AWS. You can go straight to the bucket, or search and explore for data through one of the eight recommended featured… Read More »Sentinel 2 level 2a data from AWS
This is the final part in a series on using Planet’s Open California dataset. I’ve summarised it all here. Detailed mapping of building footprints is fast becoming one of the key challenges/uses for very high spatial resolution Earth Observation data today. Being able to accurately acquire these footprints remotely and at speed is where the… Read More »Identifying buildings on medium resolution Satellite data using Monteverdi software
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 »Pan Sharpening Sentinel 2 with Planet data
I wrote recently about clipping data and shared an example in Beverly Hills. I wanted to use this data to explore and compare all the data sets available on Planet Explorer, but focusing on the terrific Open California data. I’ve been meaning for sometime to write a comparison between different sensors. My feeling is that… Read More »Open California data & sensor comparison
This week I have been thinking about image stretching. I want to start with a quote taken from the concluding remarks of Image Processing and GIS for Remote Sensing. Image Processing can never increase the information provided in the original image data, but the use of appropriate image processing can improve visualisation, comprehension and analysis… Read More »Contrast Limited Adaptive Histogram Equalization (CLAHE) with Sentinel 2a