A combined segmentation and pixel-based classification approach of QuickBird imagery for land cover mapping

Wang, Jianmei; Li, Deren; Qin, Wenzhong

doi:10.1117/12.654785

Cited by 1 publication

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“…At the same time the relationship on same scale can be expressed as region adjacent graph (RAG). In [7][12], region merging constrained by spectral and shape character based on RAG is used to reduce over-segmentation of initial watershed transform. In this paper we develop this method.…”

Section: Multi-scale Image Segmentationmentioning

confidence: 99%

Multiscale object-oriented change detection over urban areas

Wang

2006

Geoinformatics 2006: Remotely Sensed Data and Information

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Urban growth induces urban spatial expansion in many cities in China. There is a great need for up-to-date information for effective urban decision-making and sustainable development. Many researches have demonstrated that satellite images, especial high resolution images, are very suitable for urban growth studies. However, change detection technique is the key to keep current with the rapid urban growth rate, taking advantage of tremendous amounts of satellite data. In this paper, a multi-scale object-oriented change detection approach integrating GIS and remote sensing is introduced. Firstly, a subset of image is cropped based on existing parcel boundaries stored in GIS database, then a multi-scale watershed transform is carried out to obtain the image objects. The image objects are classified into different land cover types by supervised classification based on their spectral, geometry and texture attributes. Finally a rule-based system is set up to judge every parcel one by one whether or not change happened comparing to existing GIS land use types. In order to verify the application validity of the presented methodology, the rural-urban fringe of Shanghai in China with the support of QuickBird date and GIS is tested, the result shown that it is effective to detect illegal land use parcel.

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Section: Multi-scale Image Segmentationmentioning

confidence: 99%