I plan to perform an object-based land-cover classification for current vegetation types. The main goal is to create a vegetation map. I am considering to make an object based image segmentation based on 0.2 m orthopfotos and then do the classification step in combination with satellite imagery (because there are more sectral bands).

I worked out a legend (interpretation key) for my classification. I have ground control points of several field surveys. However, I don’t have such data for all classes in my classification key. What would be a general solution to compensate this problem? Is it common to replenish the lack of data simple by image interpretation (for example, CIR)?

I have available Orthophotos (also CIR), satellite imagery (in most cases preprocessed), reference data (LULC and vegetation field data)

Software: eCognition, Erdas imagine, ArcGIS, QGIS,

I know this question is probably too broad and may be difficult to answer. As a beginner I'm interested in basic steps to achieve an acceptable result. What tips can you give for crating the interpretation key and for designing a workflow? I'm also looking for initial literature for this topic (recent rewiew papers, methodology).

Similar questions and discussions