GithubHelp home page GithubHelp logo

Comments (3)

jkibele avatar jkibele commented on August 19, 2024

It's hard to say for sure what the problem is, but here's my best guess. I think that all of the training pixels are getting masked. That .compressed() bit only returns unmasked pixels. Try changing lyzdepth = de.lyzenga_depth_estimation() to lyzdepth = de.lyzenga_depth_estimation(n_std=3). If that works, you could try smaller values too. ...or if it doesn't you could try larger values.

If that does work and get you results, then I'm guessing that maybe you're working in temperate waters? There's something that's been called "over deduction" in the literature. I tried to address it in the data preprocessing section and the discussion of my depth estimation paper. It's difficult to explain clearly, but it's basically just that really dark bottom types cause the Lyzenga method to try to take the log of negative numbers. Where that happens, my code masks out those pixels. If too many pixels got masked, your error could be the result. I had to use a n_std of 2 for the Lyzenga method in my paper.

Please let me know if n_std fixes your problem. ...and please keep me posted if you get some comparable results. I'd love to know how the KNN method works vs. the Lyzenga method in other parts of the world.

from opticalrs.

CyanBC avatar CyanBC commented on August 19, 2024

Thank you for the direction. I'll keep pushing on it. My preliminary results for KNN for the waters off Okinawa (Motobu region) have been very, very good. My colleagues here have used Lyzenga processed through a different method that was not very convincing.

from opticalrs.

jkibele avatar jkibele commented on August 19, 2024

That's great to hear. Please keep me posted.

Also, if you're interested in habitat mapping, please check out my thesis. I intend to get a couple more publications out that'll put that work into a more concise format, but I've been too busy to make much progress. There's a lot more in OpticalRS than depth estimation.

from opticalrs.

Related Issues (9)

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.