A MULTIRESOLUTION REMOTELY SENSED IMAGE SEGMENTATION METHOD COMBINING RAINFALLING WATERSHED ALGORITHM AND FAST REGION MERGING

Nowadays object oriented image analysis becomes a hot issue in the field of image processing and interpretation because of its more robust noise removing ability, more abundant image features and expertise knowledge involved in analysis. The first and most important step of object oriented image analysis is image segmentation, which segments an image into many visual homogenous parcels. Based on these parcels, which are ‘objects ’ not ‘pixels’, more features can be involved which facilitates the succeeding image interpretation. In this work, a multi-resolution image segmentation method combining spectral and shape features is designed and implemented with reference to the basic ideas of eCognition, a famous object oriented image analyzing software package. The algorithm includes the following steps. 1) The initial segmentation parcels, so called the ‘sub feature units ’ are obtained with rainfalling watershed algorithm for its fast speed and pretty good initial segmentation effects. 2) A fast region merging technique is designed to merge these sub feature units in a hierarchy way. A scale parameter is used to control the merging process, which stops a merge when the minimal parcel merging cost exceeds its power. A multi-resolution segmentation can be implemented with different scale parameters, for smaller scales means less cost while merging which create smaller parcels, and vice versa. Several experiments on high spatial resolution remotely sensed imagery are carried out to validate our method