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Speckle Reduction for Small Feature Detection
Takako Sakurai-Amano1 and Joji Iisaka2
1Institute of Industrial Science, University of Tokyo
7-22-1 Roppongi, Minato-ku, Tokyo 106, Japan
Tel: +81-3-3402-6231ex2643 FAX: +81-3-33423-2834
E-mail: takako@tkl.iis.u.-tokyo.ac.jp
2Canada Centre for Remote Sensing
588 Booth St., Ottawa, Ontario, Canada K1A 0Y7
TEL: +1-613-947-1237 FAX: +1-613-947-1383
E-mail: Joji.Iisaka@geocan.emr.ca
Abstract
Many key feature that must be detected from SAR images are smaller or thinner than resolution size, especially in multi-look images. Most of previously developed de-speckling filters, in addition to speckle noise, also smooth out these small features because they were designed to extract large surface area like features. In other to extract small/thin features, the SFP (Small Feature Preserving) filter, developed to extract small and thin features, preserves such features fairly well while reducing speckle. Furthermor, a method to evaluate filters, using real images, was also developed during the course of this study. We compared the SFP filter with seven existent filters such as Lee filter, Frost filter, Sigma filter using a 3 look JERS-1image. We found that the SFP filter is the most appropriate filter to extract features brighter than the background, while the Sigma filter is the best one for features darker than the background.
1. Introduction
Many key features that must be detected from SAR images are small or thin. Several major difficulties arise when trying to detect those small/thin terrain features automatically from SAR imagery. The largest difficulty is that the back-scattering intensities of terrain objects are not unique. They are determined by such factors as the dielectric constant of terrain material. For example, a quiet water body usually appears s a very dark object in SAR imagery, Buty once the surface becomes rough, it no longer appears very dark. If disturbance is relatively small, it may appear relatively dark. But if the look angle is right and the surface is rough (a fall or a wind disturbed surface), it may appear even very bright. Thus we cannot distinguish different terrain objects only from back-scattering intensities. We have successfully integrated spatial and geometrical information as well as spectral information for the analysis of SAR imagery [1-4].
Second, the corresponding terrain features for those small/thin features are quite often smaller or thinner than the resolution size. For example, while width of an ordinary two lane road is less than 10m, the resolution of 3 look JERS-1 and 6 look ERS-1 images are about 18m and 30m, respectively. The corresponding resolution cell contains not only the road segment but also neighboring objects. It never appears as a continuous line with a constant intensity and width. It sometimes appears dissolved in the background or merged with neighboring objected.
There is also a problem of evaluating speckle filters. Evaluation of speckle are usually done using simulated images. These images comprise simple geometrical patterns of constant intensity, in which speckle noise is added using a simple multiplicative noise model. The evaluation on real image has been done only visually. But a real image is much more complex than simulated images as mentioned in the previous paragraphs. The good results obtained from simulated images are not often applicable to real images. There is a urgent need for a method to evaluate filters using real images.
This paper briefly describes the speckle reduction filter characteristics that is best suited to automatically detect small/thin features. This paper also describes the SFP (Small Feature preserving) filter developed during the course of this study. A method to evaluate filters are also briefly described. Eight filters including the SFP filter are evaluated and compared with each other.
2. Speckle Reduction for Small Feature Detection and the SFP Filter
Speckle is a phenomenon inherent in coherent imaging [13]. I theory, this "speckle" noise is multiplicative and proportional to pixel intensity. Many despecking filters have been developed, but the performance was not always satisfactory for the purposes of which the image was to be used.
The most important feature of speckle reduction filters for small/thin feature detection should be to reduce speckle yet to preserve small/thin features that are often of resolution size of smaller, Often, however, the only difference between relevant features and speckle is their spatial distribution. Unlike speckle, features of interest from ordered patterns, not random patterns. Since backscattering intensities are determined by many factors, intensity reconstruction is not the prime objective in filter development. Shape preservation is more important. Therefore main stream statistical filters may not work very well for this purpose. Spatially non-liner filters may be a solution.
Other features of a speckle reduction filters useful for the automated detection of small and thin features are :
Robustness Images usually comprise many different scenes, and the features of interest are not exclusive to one or another of the scene. For the purpose of automated detection of features, therefore, it is not desirable to use a filter that requires parameters too sensitive to scenes, especially when corresponding terrain features are smaller/thinner than resolution size. Since each resolution cell contains not only the features of interest but also those of surrounding are too sensitive, this will create yet another more difficult problem of determining the scene boundaries automatically.
Consistency It is also desirable to have a filter which shows the consistent performance for various situations. We prefer a stable filter to an unpredictable filter that sometimes performs superbly.
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