#Ecognition developer code#
The result is a unique approach to translate mind models (why a human interpreter can see the objects, changes, or features in the geospatial data) into computer understandable code (Rule Set) or an individual/customized application. To build an analysis solution, it is possible to flexibly combine the image interpretation steps like object creation (segmentation), object classification (knowledge based, fuzzy logic, machine learning), object detection (template matching) and object modification (fusing, smoothing, orthogonalization, simplification) into a Rule Set or even a new application (Rule Set with UI) to solve the analysis problem. What’s more, the extracted information is fully quantified and qualified to meet the user’s specific demands.
#Ecognition developer series#
However, it adds significant value to this information by creating a powerful cognition network in a series of iterative segmentation and classification steps.This dramatically enhances the value of intelligence and information extracted from the data. Machine learning and deep learning approaches can be easily integrated into automated workflows. Of course, it also recognizes pixel information. Rather than examining stand-alone pixels or points, it distills meaning from the objects’ connotations and mutual relationships, not only with neighboring objects but throughout various input data. ECognition classifies and analyzes imagery, vectors and point clouds using all the semantic information required to interpret it correctly.
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