Study On Mean Shift Segmentation And Application Of Remotely Sensed Imagery

With the development of remote sensing technology, abundant and various styles remote sensing data has been obtained from current earth observation system. Existing investigations show that there are many deficiencies in the process of data being translated to information, and remote sensing image segmentation is a key technology and difficult task of remote sensing image processing field. Large numbers of segmentation approaches have been developed, and some research progeny have been gained, but when these approaches are used to remote sensing image segmentation, there are also some defects such as limited adaptability, low segmentation efficiency, and low segmentation precision. Aiming at these defects and in order to improve segmentation precision and reliability, this paper improved classical Mean Shift algorithm to segment remote sensing images by fully useing multidimension feature of remote sensing images, it can be robustness to noise adaptively, as well as preserving edge information of object target effectively. The contents of this paper mainly include:(1) In order to solve the low segmentation precision of Mean Shift algorithm in the process of segmenting remote sensing images, a segmentation approach utilizing texture features and adaptive bandwidths is proposed, Classical Mean Shift algorithm only uses the spatial-range features, and can easily lead to lower segmentation precision. While the proposed method uses the features of spatial-range-texture to form multi-dimension features spaces, and develop adaptive bandwidth strategy. Firstly data clustering is carried out in the space of position-range; then spatial bandwidth, range bandwidth and texture bandwidth of each region are calculated according to previous clustering results; lastly segmentation results are gotten by adaptive clustering in the space of position-range-texture. Experiment results show that the proposed method can improve segmentation precision of remote sensing images with high adaptability and robutness.(2) In order to overcome the over-segmentation of classical Mean Shift algorithm, a region combination method is developed to postprocess the initial over-segmentation images. Firstly, spatial bandwidth is selected according to the resolution of remote sensing images under study; then spectrum bandwidths of each band are estimated by using plug-in rules; lastly, segmented regions were merged by using regions areas weighed similarity rule and region entropy based region merge stopping rules to solve over-segmentation problem of classical Mean Shift algorithm.(3) An improved fast segmentation method is proposed in this paper, in order to solve long iterative time of classical Mean Shift algorithm, which is not apapt to mass remote sensing imageries. Aiming at fast segment remote sensing imagry, some accelerating strategies were proposed to solve each issue which affects time complication of classical Mean Shift algorithm. Firstly, super-pixels are gotten by using fixed bandwidths Gauss Mean Shift cluster algorithm. Then dandwidth of each super-pixel is calculated adaptively. Finally, remote sensing images segmentation is performed by using region-based super-pixels fusing process, and then high precision segmented results can be obtained.(4) Mature segmentation evaluation method is adopted to evaluate remote sensing image segmentation. Martin error measure method and object-level consistency error meature method are contrastively analyzed firstly. The comparison experiments show that the object-level consistency error meature method works at the object level and can effectively measure the discrepancy between a segmented image and the reference image. Compared with Martin error measure method, object-level consistency error meature method can correctly reflect over-segmentation and under-segmentation of segmented images, and its evaluation result can be consistent with the subjective evaluation much better.(5) A roads extraction method based on Mean Shift segmented remote sensing image is proposed. Firstly, road images are initially segmented by using Mean Shift algorithm, and regions with similar gray values are merged, and binarization segmentation is completed by selecting the optimal thresholds based on histogram of segmented images. Then shape indices are used to remove those regions mixed in image which have different shapes comparing to road; in order to ensure the independence of each road target candidate, a multidirectional morphological filtering algorithm is designed to separate road from the neighboring non-road objects, and then road lines are extracted. Finally, road network is extracted by connecting the broken road lines. Several experimental results show that the proposed method can be used to extract roads network from remote sensing images even under complex conditions, especially for the straight roads.At last, after concluding all research work in this paper, further work need to be in-depth studied:(1) Consider multi-scale factors of remote sensing, and realize multi-scale remote sensing image segmentation based on Mean Shift algorithm.(2) Consider extracting textures features by using Gabor filter, or use more features such as shape features to segment remote sensing images based on Mean Shift algorithm.