Information Extraction of High-Resolution Remotely Sensed Image based on Multiresolution Segmentation
The principle of multiresolution segmentation was represented in detail in this study, and the canny algorithm was applied for edge-detection of remotely sensed image based on this principle. The target image was divided into regions based on objectoriented multiresolution segmentation and edge-detection. Further, object hierarchy was created, and a series of features (water bodies, vegetation, roads, residential areas, bare land and other information) were extracted by the spectral and geometrical features. The results indicates that edge-detection make a positive effect on multiresolution segmentation, and overall accuracy of information extraction reaches to 94.6% through confusion matrix.