Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques
Marco GianinettoLaboratory of Remote Sensing (L@RS), Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133, Milano, ItalyMarco RusminiGabriele CandianiInstitute of Electromagnetic Sensing of Environment (IREA), Italian National Research Council (CNR), Via Bassini 15, 20133, Milano, ItalyG. Dalla ViaLaboratory of Remote Sensing (L@RS), Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133, Milano, ItalyFederico FrassyLaboratory of Remote Sensing (L@RS), Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133, Milano, ItalyPieralberto MaiantiLaboratory of Remote Sensing (L@RS), Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133, Milano, ItalyAndrea MarchesiLaboratory of Remote Sensing (L@RS), Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133, Milano, ItalyFrancesco Rota NodariLaboratory of Remote Sensing (L@RS), Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133, Milano, ItalyLuigi DiniCosmo Sky Med-CIDOT Center for the Interpretation of Earth Observation Data, Italian Space Agency, Space Geodesy Center “G.Colombo”, C/da Terlecchia, c.p. 11, 75100, Matera, Italy
2014en
ABI
Аннотация
Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen's kappa coefficient of 0.84 and an overall accuracy of 85%.
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