Scale computation on high spatial resolution remotely sensed imagery multi-scale segmentation
| Autor: | Jianhua Liu, Zhengyuan Mao, Mingyi Du |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
Pixel Scale (ratio) business.industry Computer science Segmentation-based object categorization Computation ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Scale-space segmentation 02 engineering and technology Image segmentation 01 natural sciences Distributive property Computer Science::Computer Vision and Pattern Recognition General Earth and Planetary Sciences Segmentation Computer vision Artificial intelligence business 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | International Journal of Remote Sensing. 38:5186-5214 |
| ISSN: | 1366-5901 0143-1161 |
| Popis: | Scale computation for multi-scale image segmentation is an important research area in geographic object-based image analysis (GEOBIA). For the highly spectral heterogeneity of high spatial resolution remotely sensed imagery (HSRRSI), and the changing sizes of geographic features and their spatially distributive patterns, it is difficult to build a global or local-scale calculation parameter model to effectively guide multi-scale segmentation parameters setting in large-scale regions. Usually, the segmentation parameters are used to measure the heterogeneous and homogenous adjacent pixels in spatial and spectral spaces simultaneously. It has been proved that the adaptive acquiring parameter of scale plays a key role in gaining precise segmentation results, and later it deeply influences the automatic recognition and post-processing of the physical image parcels (PIPs). However, in most cases, scale computation techniques still fail to guide segmentation to produce appropriate or repeatable results ... |
| Databáze: | OpenAIRE |
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