We developed a SFP (Small Feature preserving) filter to reduce speckle prior to he automated detection of small/thin features[15]. In stead of using popular statistical methods, the SFP Filter use a spatially non-linear filtering method similar to the Sigma filter[8] and a filter developed by Ali and Burge[12]] to preserve shape information. The estimate of the center pixel value of a moving window is given by
Here Z
c,Z
ki and Z
c are respectively the value of the center pixel, the value of the window pixel at k, 1, and the average pixel value of the window pixel,
sv is the standard deviation of the multiplicative speckle noise. It can be estimated theoretically or measured from the image. 2m+1 and 2n+1 correspond to the window size. Unlike the Sigma filter and the filter developed by Ali and Burge, this filter does not have a mechanism of suppressing the right side expression of Equation (2). In this way, we can avoid lowering the average values of featureless areas in the Sigma filter Various small or thin features have been successfully from several spaceborne SAR imaging using this filer [16-18]
3. Evaluation Method and Experiment
Recently Lee et al. evaluated various speckle filter quantitatively using simulated images[14]. Evaluation of the filters applied to real images have been done only visually. They recommended small windows to extract small features. A smaller window, however, gives a good appearance but it is not necessarily good for automated detection of those features because small features from the noise. Current image processing techniques, however, cannot detect small features without reducing noise. Therefore the evaluation of filters should include not only noise reduction in the background but also the contrast preservation of the object of interest.
In a previous paper [19], we proposed a new evaluation method of despecking filter for the detection of slightly bright small features sing ship wake features in a 6-look ERS-1 image as an example. This method uses real images and evaluated the filters for the detectability of small/thin features by integrating both an ordinary speckle reduction measure and a new measure for the contrast of target features and their immediate background. As a measure of
Speckle reduction, we employed
b defined by Lee et al. [14]. The key of the new contrast measure is the automatic creation of feature masks. Those feature masks are automatically created from a real image by binalzing at multiple threshold levels and the immediate background masks are also automatically created by dilating the feature area and the immediate background area. We summarized the results to demonstrate the abilities of several filters using the average ratios and average difference for eight randomly selected features among all measured features at three different threshold levels.
In this study, we applied a similar method to more filters and studied more features systematically. We used a 6000 pixel by 6400 line 3 look JERS-1 image of the Mt. Fuji area of Japan acquired on April 23, 1992. This image was processed by taking a square root of the sum of three independent look images. We evaluated eight filters for the detestability of both bright and dark small/tin features. Five small subimages (about 100x 100 pixels) of relatively featureless water areas were selected for the measurement of speckle reduction. For the contrast measurement, 19 bright features and 19 dark features were selected from three sub-scenes of different contrast in the image shown in Figure 1.
Figure 1. 19 bright features and 19 dark features were selected from three different sub-scenes for the evaluation of contrast. Image intensities were arbitratily stretched to provide a good appearance for each scene.
The Median filter, the Frost filter [7], the Lee filter [5,6], the Enhanced Lee filter [9], the Gamma MAP filter [10],the Sigma filer [8] and he Weighing filter [11] were compred with the SFP filter. We did not change some scene dependent parameter values scene by scene because there was no automatic way of defining scene boundaries in the entire image.
4. Results and Discussion
4.1. Speckle reduction in featureless regions
To find the ability to reduce the background speckle noise, we measured the degree of speckle reduction in 5 featureless areas using b values, the square root of the variance of the image divided by the mean, defined by Lee et al. [14]. The results could be classified into three groups as shown I Figure 2(a). The first group of filters reduced speckle very well, almost three fourths of the original images. The Median filter, the Frost filter, the Enhanced Lee filter and the Gamma MAP filter belonged to this group. The second group of filter were not as good as the first group but still fairly good and reduced speckle more than 1/2 . This group included the Lee filter, the Sigma filter and the SFP filter. The last group, the Weighting filter, reduced only slightly more than ¼ of original speckle noise.
Figure 2 Speckle reduction in 5 featureless areas
Figure 2(b) shows the average pixel value for five relatively featureless areas. The values were normalized to the average values in the corresponding original pixel values. The average values of Frost filter, the Lee filter and the SFP filter were almost equal to the original average. The enhanced Lee and the Gamma MAP filter showed slightly higher values. The Median, the Weighting and the Sigma filter deviated to lower values.
4.2 Contrast and intensity of small features
The average target intensity It(i,j,f) and the average background intensity Ib (I,f,f) of a target feature at a certain threshold level were measured for 19 features high than the background and 19 features darker than the background in each filtered image. Here, i,j, and f indicates a feature, a threshold level and a filter, respectively. The following ratio Rf (i,j) can be defined as a measure of contrast.
Were o indicate the original image. Although we measured the ratios at several threshold level for each features, we found that values of the ratio do not vary very much. Those values for each features can be determined within an error of approximately 0.05. Therefore we selected a middle level (not close to the background average value) threshold value between the average target intensity and the average background intensity to represent the ratio of each feature.
Brighter feature Figure 3 summarizes the results for the features brighter than the backgrounds. A thick line connects the ratio R
f at a certain level for each of 19 features. The vlues closer to 1 means that the original contrast of the target to the background was maintained. A thin line shows a variation of the intensity average of target features.
Figure 3 Preservation of the contrast and the intensity of the bright features.
There were four groups in this category. The Median filter and the Frost filter belonged to the first group. The contrast wavy very low, sometimes negative, indicating a poor ability of detecting small/thin features. The second group consisted of the Enhanced Lee filter and the Gamma MAP filter Those filter were excellent for some features, but very poor for other features. The third group consisted of the Lee filter and the Sigma filter. They performed only fairly for all features. The fourth group were the Weighting filter and the SFP filter. Those filters performed very well for all 19 features. The Weighing filter preserved not only high but also fairly constant contrasts for all features. The SFP filter surpassed the Weighting filter for the most features, but no for some features.
As for the target intensities as shown in thin lines in Figure 3, the Weighing filter and the SFP filter consistently preserved high although always slightly lower than original values. The Enhanced Lee filter and the Gamma MAP filter were not so consistent as the first group although they sometimes preserved the original values. The remaining four filters showed similarly low values. The Frost sometimes performed even worse.
