Object-oriented landslide detection from remote sensing imageries with high resolution

Remote sensing detection of the landslide is very important for hazard investigation and mapping,and object-oriented technology is one of these methodologies to extract thematic information from high resolution remote sensing imageries.In this paper,the study area is located in the tropical area where the earth surface is covered with high rainforest canopy,so the landslide scars can be recognized from remote sensing imageries more easily.Based on the feature analysis of landslide scars from these remote sensing imageries,the object-oriented landslide detection methodology is presented with regard to high resolution remote sensing imageries.Firstly,two-period remotely sensed imageries are segmentated into multi-scale and different levels,so the basic units of image objects for land cover change detection have been defined,and the same time,the multiple-level object hierarchy network has been constructed for landslide detection.Followed,with image object characteristics,environmental context and object topological relationship,the knowledge rule of landslide detection suiting for this experimental area is constructed,thus the whole detection procedure is contained into image object analysis and processing,as a result,the landslide detection from high resolution remote sensing imageries has been realized.The result shows that this landslide detection method based on image objects is feasible to hazard investigation and mapping in the tropical rainforest area,and this object-oriented methodology may give a reference to hazard research and investigation in other geographical environment where is similar to this area.