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How to perform image segmentation in Google Earth?
I am trying to perform an image segmentation using Google Earth Engine. I have performed OBIA in the past using python and scikit-image (skimage.segmentation.felzenszwalb) , but not sure how to do this using GEE.
How to use EE obia for image segmentation?
Here is an EE OBIA example based on Noel Gorelick’s recent image segmentation presentation and example code ( available here ). Note that you’ll need to adapt this to your particular input dataset and use case.
How to use Google Earth for image classification?
In this post we’ll perform a classification of high resolution satellite images using GEOBIA (Geographic Object Based Image Analysis) on Google Earth Engine. The satellite image to be classified is an image from the National Agriculture Imagery Program (NAIP), which contains the bands B (Blue), G (Green), R (Red) and N (Near Infrared).
How to perform object based image classification in Google?
How do I convert the ‘edges’ image to a 1-band raster with N objects, with the individual objects being grouped by similar spectral values? Here is an EE OBIA example based on Noel Gorelick’s recent image segmentation presentation and example code ( available here ).
How is image classification used in Google Earth?
One of the ways is by implementing image classification. Image classification functions to extract information based on the information within the pixel of the image. The raster then can be transformed into a thematic map. The most common practice for classifying the image is to understand the landcover within an area.
How does supervised classification work in Google Earth Engine?
An error occurred while retrieving sharing information. Please try again later. The Classifier package handles supervised classification by traditional ML algorithms running in Earth Engine. These classifiers include CART, RandomForest, NaiveBayes and SVM. The general workflow for classification is:
How to train a classifier in Google Earth?
Train the classifier using the training data. Classify an image or feature collection. Estimate classification error with independent validation data. The training data is a FeatureCollection with a property storing the class label and properties storing predictor variables. Class labels should be consecutive, integers starting from 0.