I am new to the field of Machine Learning and I want to know what all way I can implement machine learning to classify any satellite image.

closed as too broad by Spacedman, lynxlynxlynx, BERA, Oto Kaláb, mgri Oct 19 '17 at 9:46

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

  • This is a very broad and vague question, no answer here is ever going to give you everything about machine learning and image classification. There's probably books on the subject. – Spacedman Oct 19 '17 at 7:23
  • You can see a tutorial I've put together on using scikit-learn to classify a satellite image here: github.com/patrickcgray/open-geo-tutorial/blob/master/Python/… – clifgray Jan 1 at 17:28

I would suggest looking at a few of these resources and continue searching similar documentation related to those topics related to ML, Python and R.


Researchers have demonstrated steady progress in computer vision by validating their work against ImageNet -- an academic benchmark for computer vision. Successive models continue to show improvements, each time achieving a new state-of-the-art result: QuocNet, AlexNet, Inception (GoogLeNet), BN-Inception-v2. Researchers both internal and external to Google have published papers describing all these models but the results are still hard to reproduce. We're now taking the next step by releasing code for running image recognition on our latest model, Inception-v3.

Inception-v3 is trained for the ImageNet Large Visual Recognition Challenge using the data from 2012. This is a standard task in computer vision, where models try to classify entire images into 1000 classes, like "Zebra", "Dalmatian", and "Dishwasher". For example, here are the results from AlexNet classifying some images:

enter image description here


A tutorial

In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class you want to be able to recognize.


Accessed via R and Python APIs, pre-trained Deep Learning models and Transfer Learning are making custom Image Classification with large or small amounts of labeled data easily accessible to data scientists and application developers. Our instructors will walk you through creating end-to-end data science solutions in R and Python on virtual machines, Spark environments, and cloud-based infrastructure and consuming them in production. We will cover strategies and best practices for porting and interoperating between R and Python, with a novel Deep Learning use case for Image Classification as an example use case.

Not the answer you're looking for? Browse other questions tagged or ask your own question.