research on multi-scale segmentation parameter selection during the object-oriented remote-sensing i

1、<p>　　Research on multi-scale segmentation parameter selection during the object-oriented remote-sensing i</p><p>　　Abstract. with the appearance of high spatial resolution remote-sensing image, the obj

2、ect-oriented image analysis technology provides new thinking and approach. The multi-scale segmentation is one of the key technologies for the object-oriented high spatial resolution remote-sensing analysis. This paper s

3、tudies the effect of parameters such as color, shape and segmentation scale on the image segmentation quality based on the heterogeneity minimization region-merging algorithm in multi-scale segm</p><p>　　Key

4、 words: object-oriented, multi-scale segmentation, segmentation parameters </p><p>　　1. Introduction </p><p>　　The high spatial resolution remote-sensing has abundant spatial information and cle

5、ar detail texture information. Adopting traditional pixel-based spectral information extraction technology can not satisfy the demand any more, while the object-oriented image analysis technology provides new thinking an

6、d approach for high spatial resolution remote-sensing image information [1]and has been a research hot-spot in remote-sensing image processing field with the key technologies including image segmen</p><p>　　

7、2. The region-merging algorithm based on the heterogeneity minimization </p><p>　　The basic idea of heterogeneity minimization region-merging algorithm is to emerge the single neighbor pixel into lots of sma

8、ll image objects by heterogeneity minimization, then emerge the small image objects into larger image until the objects forming polygon objects eventually. The heterogeneity of image objects f can be represented as: <

9、/p><p><b>　　(1) </b></p><p>　　In the formula, hcolor refers to spectrum heterogeneity factor; hshape refers to shape heterogeneity factor; wcolor refers to spectrum factor weight, wshap

10、e refers to shape factor weight and 0≤wshape≤1， 0≤wcolor ≤1，wshape+ wcolor =1. 　　3. The multi-scale segmentation parameter selection </p><p>　　The setting of various parameters is very important in the proc

11、ess of multi-scale image segmentation. The multi-scale segmentation parameters include varying color factor, shape factor and segmentation factorcolor. The construction of multi-scale parameters, see figure 1. </p>

12、<p>　　3.1 Color factor </p><p>　　Color factor reflects the spectrum information that is the main information contained in the image data[6]. The weight setting is one of the important elements to affe

13、ct the segmentation quality. In the process of image segmentation, the color factor is the most important factors to generate image object with the larger weight setting. </p><p>　　3.2 Shape factor </p>

14、;<p>　　Shape factor helps to avoid too fragmented image objects and can avoid the phenomenon of “same object” with different spectrum”, “same spectrum with different objects” and “salt & pepper noise” so as to

15、 improve the segmentation quality and information extraction precision[7]. The shape factor is comprised by compactness and smoothness, of which the compactness is used to separate the compact and non-compact target area

16、s but the smoothness is used to describe the border smooth degree of image obj</p><p>　　Due to the fact that the sum of color factor and shape factor is 1, the high weight setting of shape factor lowers the

17、color factor weight, which is not beneficial to information extraction. Therefore, to maximum the weight setting of color shape should be followed in the process of image segmentation; the necessary shape factor should b

18、e used at the time of segmentation image objects[7]. </p><p>　　Those variable setting should be in line with actual features requirements, because if not consider the shape information but set the color fact

19、or of 1, the image object will be generated with the narrow zigzagged spectrum; if only emphasize the shape information but ignore the spectrum information, the generated image do not have formation[8]. The segmentation

20、quality with scale of 40, compactness of 0.5, smoothness of 0.5 and the comparative color factor of 0.9 and 0.1 respectively, see figure</p><p>　　As showed in figure 2, setting too small color factor weight

21、and focusing the shape factor will lead to the massive loss of features color information, which causes the less-than-ideal image quality after segmentation. Therefore, it is needed to set larger color factor weight. The

22、 effect of scale 40, color factor 0.9 and comparative compactness factor 0.9 and 0.1 on segmentation quality, see figure 3. 　　As can be seen in figure 3, the relatively small compactness parameter will cause the over c&

23、lt;/p><p>　　3.3 Segmentation scale </p><p>　　The selection of segmentation scale is very important, which determines the size of image objects, the quality of segmentation and the precision of info

24、rmation extraction directly. Hence, the reasonable parameters should be set to compare the quality of segmentation image under different scales before multi-scale segmentation. This paper found out a group of best segmen

25、tation parameters through a large number of experiments. The segmentation result at the time of color factor 0.8, shape factor 0</p><p>　　As can be seen in figure 4, when the segmentation scale is small, the

26、 features in the image will be over segmented; when the larger segmentation scale is selected, the “vague” image of small features in image will not be beneficial to the small area extraction of features. Hence, the scal

27、e is determined by type characteristics of the extracting features with the small area of features selecting relatively small segmentation scale and large area of features selecting relatively large segmentation</p>

28、;<p>　　4. Conclusion </p><p>　　It can be seen that every parameter will have an influence on segmentation quality in the image segmentation. Therefore, in the practical application, the reasonable set

29、ting and selection of segmentation parameters should be in line with image characteristics, different features characteristics so as to make the image quality more real and objective after segmentation. </p><p

30、>　　5. Acknowledgements </p><p>　　This article is completed with the fund of the Key Laboratory of Water Environment Evolution and Pollution Co- ntrol in Three Gorges Reservoir(serial number is 2012QN-07 )

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