Sentinel_bot – now with NIR vision

A quick blog post as I’m very much in the throes of writing! I took a few minutes today to introduce false colour (Near Infrared – Red – Green) images into @sentinel_bot’s programming, so now there’s a 20% chance that an image it produces will be false colour. In the near future I think I’ll introduce other band combinations (such as PCA band combos for mineral contrast enhancement), but for now I’m going to let it sit and appreciate some of what it comes up with, such as the image below.

Source : https://github.com/JamesOConnor/Sentinel_bot

Twitter : www.twitter.com/sentinel_bot

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NIR – R – G image over Argentina

Neural nets in Remote Sensing

Neural nets, a summary: (The chain rule * your GPU RAM)

Around 2 years ago I remember having a discussion with Jan Boehm about photogrammetry after my first meeting as the shadow wavelength rep on the Remote Sensing and Photogrammetry committee. He mentioned Agisoft, which I was already using and familiar with at the time, but then mentioned the movement in dense matching algorithms towards use of neural nets, mentioning one which had been submitted to the KITTI stereo benchmark.

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Disparity map using Žbontar’s methods

This piqued my curiosity, and I remember reading and being quite excited by Jure’s paper. While some concepts were new to me, the use of Convolutional neural networks (ConvNets) and the two types of architecture used to initialize the initial results, before moving towards post-processing using semi-global matching. I remember sinking a great deal of time into reading about the methods, exploring the github and methods used within the core of the paper, and subsequently hounding a colleague who was using a Titan-X for some deep learning work for some time with it.

I remember I took the ideas with me to EGU 2016, and even went to the point of acquiring a data set I thought would be worthy of testing it with from a German photogrammetrist, Andreas Kaiser. Alas, it wasn’t to be due to the hardware limitations and the fact that I wasn’t very familiar with the lua programming language. However I had learned a lot about the nature of deep learning, which I felt was a decent investment of my time.

The reason for this blog entry, however, isn’t to enlighten the reader of my failure to get up to speed with neural nets at the time, it’s much more hopeful than that! Fast forward two years, and development within the field of deep learning has come on leaps and bounds. With serious development time going into TensorFlow, and a beautiful and accessible front end in the form of keras, the python user really does have the tools to apply neural nets to all sorts of applications within image-based studies.

Having learned the basic ideas around neural nets from my initial excitement a long time ago I decided to try and get involved with the community once more. A few months back, a well timed kaggle competition came up which involved image classification, which raised an eyebrow. I contacted an old friend of mine who had just finished his PhD in medical imaging and we set to take up the challenge.

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The task for the competition involved labeling satellite imagery

Since starting the task, I feel like I’ve come on leaps and bounds with not only the concepts behind ConvNets, but their architecture and application in the python framework. Whilst we generated lots of code (will be on github in due course), and had lots of ideas floating about, we finished a decidedly average mid-table – this first pass was as much an experience in learning about organisation as well as about imaging science, but it’s made me rethink about using ConvNets in a Remote Sensing/Photogrammetry environment.

Whilst we are seeing more contributions coming out of the community, and the popularity of other less technical concepts like support vector machines have shown I’m hoping to extend my skill set to include all of these in the future. If anyone who happens to be reading this feel the same, don’t hesitate to get in touch!

 

Sentinel bot source

I’ve been sick the last few days, which hasn’t helped in staying focused so I decided to do a few menial tasks, such as cleaning up my references, and some a little bit more involved but not really that demanding, such as adding documentation to the twitter bot I wrote.

While it’s still a bit messy, I think it’s due time I started putting up some code online, particularly because I love doing it so much. When you code for yourself, however, you don’t have to face the wrath of the computer scientists telling you what you’re doing wrong! It’s actually similar in feeling to editing writing, the more you do it the better you get.

As such, I’ve been using Pycharm lately which has forced me to start using PEP8 styling and I have to say it’s been a blessing. There are so many more reasons than I ever thought for using a very high level IDE and I’ll never go back to hacky notepad++ scripts, love it as I may.

In any case, I hope to have some time someday to add functionality – for example have people tweet coordinates + a date @sentinel_bot and have it respond with a decent image close to the request. This kind of very basic engagement for people who mightn’t be bothered going to Earth Explorer or are dissatisfied with Google Earth’s mosaicing or lack of coverage over a certain time period.

The Sentinel missions offer a great deal of opportunity for scientists in the future, and I’ll be trying my best to think of more ways to engage the community as a result.

Find the source code here, please be gentle, it was for fun 🙂

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EO Detective interviews Tim Peake

I saw this on EODetective‘s twitter account – an interview with Tim Peake about the process behind the astronaut’s photography generated on board the ISS. I’ve actually used a strip of them before to make a photogrammetric model of Italy, and was very curious about the process behind their capture.

Interesting to see they use unmodified Nikon D4s – I was curious about why they were using a relatively small aperture (f/11) for the capture of the images I had downloaded, and while ISO was mentioned I’m still left wondering. I guess they don’t really think about it as they are very busy throughout the day, though he did mention they leave them in fully automatic most of the time. While you could potentially get better quality images from setting a wider aperture, as per DxoMark’s testing on 24 mm lenses, I’m guessing the convenience of using fully-auto settings outweigh the cost.

But that’s not really in the spirit of the interview, which is more to get a general sense of life aboard the ISS.

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A sample image from the ISS