How to segment images in Google Earth Engine?
For segmentation on Google Earth Engine, we will use the SNIC (Simple Non-Iterative Clustering) algorithm. Segmented Image. More About SNIC: Simple Non-Iterative Clustering (SNIC) is an improved version of the Simple Linear Iterative Clustering (SLIC)… Paper: The next step is to collect segments representing each class of land use and land cover.
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 use Geobia in Google Earth Engine?
GEOBIA’s main step is image segmentation. An Image segmented in spectrally different regions will make the algorithm classifier well define the patterns of each class, performing a good classification. For segmentation on Google Earth Engine, we will use the SNIC (Simple Non-Iterative Clustering) algorithm.
How is object based image analysis on Google Earth Engine?
As the classifier in Google Earth Engine is standardized by pixel, we define the same values of the features selected before in each pixel in a segment. Thus we use the training of the Random Forest algorithm with the selected segments and the prediction of the complete image, resulting in our mapping of land use and land cover.
Is it possible to make SNIC algorithm segmentation algorithm?
I tested my hypothesis that SNIC is sensitive to scale by exporting region in 2 scales. And to my surprise, both image did show inconsistency (large area segmentation on scale of 10 vs small area segmentation on scale of 2) Is it possible to specify scale to make SNIC algorithm segmentation on that scale only?