Evaluation of Front Detection Methods for Satellite-Derived SST and Chl-a Data Using Edge Detection Algorithms

1. IntroductionThe knowledge of sea surface temperature (SST) and chlorophyll-a over large

spatial areas in a continuous temporary base is necessary from the perspectives of both physical and biological oceanography. Satellite remote sensing is the only tool that can provide these SST and chl-a fields with high spatial and temporal resolution in a practical way. Many studies that make use of satellite-derived data depend upon the precise location of edges associated with oceanographic features (Pratt et al., 1991; Podesta et al. 1993). As in costal seas, thermal frontal regions have been shown to be associated with high primary production and corresponding large standing stocks of phytoplankton; while the chl-a fronts might reveal the events of red tides or unusual land discharge [ref?].

For many years, in spite of intensive labor, the location of edges in oceanographic images was performed manually [ref? This is before when?]. With the rapid expansion of the available data, it becomes impossible to for the processing and analysis to be done by human operators. In addition, the results obtained are subjective and lack of consistency, and therefore could not be compared by different researchers. By contrast, a single computer algorithm will always process images the same way and obtain the same results and, thus, can provide a standard of comparison.

Over the last few years, various edge detection methods have been developed and applied to oceanographic satellite images. Although the significant improvements have been made, there still remain the following challenges when using the algorithms for front detection from satellite SST and ocean color images. The method should be robust enough to overcome the impulsive noise and thus detect the smaller-scale features. Furthermore, a detected front should be defined from a point of spatial structures of the front rather than seasonally/regionally varying geophysical parameters in order to analyze the fronts consistently throughout the year [not sure what this means].

Widely used edge detection algorithm is derivative-based operators. The intensity gradient (first derivative) detectors (such as Sobel, Prewitt, Roberts, Kirsch [what are these?]) are used, for example, in oceanographic texture analysis (Sauter and Parson, 1994), in automatic detection of Gulf Stream rings (Janowitz, 1985). Second-derivative detectors, such as Laplacian/Laplacian of the Gaussian [what is this? Need brief explanation] are used in fish detection (Savage et al., 1994). These derivative-based edge operators are very sensitive to impulsive noise. They usually require a previous filtering to eliminate the noise. But the filtering inevitablely blurs the edges of the images and makes it more difficult to detect finer-scale frontal features.

Canny (1986) proposed an ‘optimal’ edge detector based on three criteria: good detection, good localization and low error rate. The algorithm first smoothes the image using a filter very close to the first derivative of a Gaussian [explain this]. It then finds the image gradient to highlight regions with high spatial derivatives and applies nonmaxima suppression to the gradient magnitude [explain this, too]. Finally, a double thresholding algorithm is used to detect and link edges. Although the Canny is a modern

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“standard” [for oceanographic features?], in the sense that papers describing new edge detectors often compare the results to those of the Canny [ref?], its application to marine satellite images has been scarce [then who used the Canny method? For what type of features?].

Recently, wavelet transform based edge detectors have been applied to satellite images to separate processes [features?] at various scales, including relative phase/location information for coastal monitoring applications (Liu et al., 1997a), for ice edge and ice floe tracking (Liu et al., 1997b), for ocean surface layer drift tracking (Liu et al, 2002), and for monitoring snowmelt using satellite passive microwave measurements (Liu et al., 2005). Wavelet transform (WT) is naturally a multiscale analysis [explain more]. Mallat and Zhong (1992) constructed a dyadic wavelet, a quadratic spline, which approximates the first derivative of Gaussian [explain more]. The corresponding dyadic wavelet transform (DWT) is equivalent to the Canny edge detection. In DWT domain, it is represented that the edge structures present observably at each subband while the noise decreases rapidly along the scales [not clear]. It has also been shown that multiplying the adjacent scales could enhance edges while suppressing noise (Xu et al., 1994; Sadler and Swami, 1999). Zhang and Bao (2002) proposed a wavelet based edge detection scheme by scale multiplication. [all these above methods need to be explained more on their principles]

Several recent studies have adopted an edge detection method developed by Cayula and Cornillon (1992, 1995), and have obtained good results in presenting frontal views of the regional seas (e.g., Kahru et al., 1995; Mavor & Bisagni, 2001; Ullman and Cornillon, 1999, 2001; Miller, 2004). The algorithm, originally developed for SST images (?), operates at three levels: picture, window, and local/pixel levels. The edge detection is based on the analysis of the temperature histogram of each window in the image, using unsupervised learning methods and statistically deciding on the existence of boundaries. [explain more on how an “edge” is found] The edge contour is then completed by region-based algorithms. The algorithm is affected by impulsive noise; therefore, previous median filtering is needed.

SUSAN edge detector (Smith and Brady, 1997) calculates the edge strength using the number of edges with similar brightness within circular masks [not clear]. The edge direction is calculated using moment calculation and nonmaxima suppression is used to thin edges [not clear]. In one work on comparison of edge detector performance through use in an object recognition task (Shin et al., 2001) [what is this object?], SUSAN edge detector was found to perform best. Holyer and Peckinpaugh (1989) applied an algorithm based on the gray level co-occurrence (GLC) to ocean satellite images [what type of image?] to detect mesoscale features. Recently, an edge detection algorithm, based on an entropic approach using Jensen-Shannon divergence [what is this], has been used to detect sea surface temperature (SST) fronts in satellite images (Vazquez and Allah, 1999; Shimada et al., 2005). [Having read this far I have no clue on how each method works]

Assessing the performance of edge detection algorithms is a difficult task because the performance depends on several factors. At a minimum, they include (Heath et al., 1997): the algorithm itself, the type of images used to measure the performance of the algorithm, the edge detector parameters used in the evaluation, and the method for evaluating the edge detectors. Any conclusions based on synthetic images have limited

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value since there is no simple extrapolation to real world images. Most methods for evaluating an edge detector rely on the specification of ground truth. This is very difficult for real images of common scenes, even more so for marine satellite images of SST and Chl-a fields. It is possible for people to label some of the edges in an image, but the difficulty in labeling all of the edges is clear to anyone who has tried. Some methods proposed (Kitchen and Rosenfeld, 1981; Zhu, 1996; Yitzhaky and Peli, 2003) can operate on real images and do not required ground truth [what are these methods? Are these new methods in addition to the ones you already described?]. As pointed out in Health et al. (1997), each of these methods leaves something to be desired [don’t know what this means]. Most significantly, some kind of estimated ground truth (EGT) produced in these methods might not necessary reflect the true edges in the images [how do we know if an edge is “true”?].

Given the various edge-detection methods, it is desirable to know which one performs better on realistic satellite imagery of SST and ocean color, and how they compare with each other under various circumstances. Unfortunately, to our knowledge such a comparison study does not exist in the current literature. Therefore, Iin this paper, we evaluated apply four edge detection algorithms to satellite images of SST and Chl-a fields on the West Florida Shelf. These algorithms include They are (1) the well-known Canny edge detector (Canny, 1986), (2) the histogram algorithm (Cayula and Cornillon, 1992), (3) dyadic wavelet transform (DWT) based edge detection by scale multiplication (Zhang and Bao, 2002), (4) SUSAN edge detector (Smith and Brady, 1997). After some preliminary evaluation, we exclude thoseThe gradient-based detectors were not included in this comparison, because they are so sensitive to noise and can not adequately detect and retain finer-scale frontal features, based on our preliminary tests over satellite SST and ocean color images. We use human judgment [why human judgment? No other better way?] for obtaining a practical evaluation on how well the above algorithms detect both mesoscale fronts and fine features in a dynamic coastal area.

2. DataThe SST satellite data were obtained from the Advanced Very High Resolution

Radiometer (AVHRR) of the TIROS-N series satellites. The images cover the West Florida Shelf, between 24° N - 30.5° N and 87.5° W - 80.5° W, with a spatial resolution of 1.4 km x 1.5 km. The presentation of temperatures in the images is 5 digital counts for 1 oC. Data products of SeaWiFS were created in IMaRS (University of South Florida) with the SeaDAS software (version 4) developed at NASA. The OC4 bio-optical algorithm of O'Reilly et al. (2000) is used to derive chlorophyll concentrations. Chl-a images in the West Florida Shelf covers the area 22° N - 31° N and 91° W - 79° W, with a spatial resolution of 1.4 km x 1.5 km.

[If you introduce the various edge-detection methods very briefly in the “Introduction” section, this section can be “Data and Methods” where you can detail those methods to make it very clear to a reader how they work. Perhaps some of the text in the next section can be moved to this “Method” section, too]

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3. Result and discussion3.1 Parameter Selection

Selecting the input parameters of each algorithm is a critical step in edge detector performance evaluation because the resulting edge quality varies greatly with the choice of parameters. A fixed number set of parameters [do you mean several sets? The word “fixed” is confusing] for each algorithm is used to find a final set of parameter which gives optimal result based on visual assessment as well as checking the gradient across the detected edges. While this approach does not guarantee that the optimal parameter set was identified, it does avoid biasing the results toward particular algorithm (Heath et al., 1997) [what does this mean?].

The Canny edge detector allows the user to specify three parameters. The first is sigma, the standard deviation of the Gaussian filter specified in pixels. The second parameter, low, and the third parameter, high, are the low and high hysteresis thresholds, respectively. The parameters tested are combinations of sigma, low and high threshold values, where sigma {0.8, 1.0, 1.5}, low {0.05, 0.1, 0.2}, high {0.1, 0.15, 0.2, 0.4, 0.6}. Low threshold values are tested for the detection of fine features. [not clear what these parameters mean. This should be in the “Method” section] The histogram algorithm (Cayula and Cornillon, 1992), the parameter (opt) is the criterion used to decide whether one or two populations are present in a given window [what is a population? What is a window?]; the parameters C1, C2 and C3 measure the cohesion of each population with a given window. The combinations of parameters tested are: (opt) {0.68, 0.72, 0.76}, C1, C2 and C {0.88, 0.9, 0.92, 0.94}, the pixel window size {16 x 16, 32 x 32, 64 x 64}. [what do these parameters mean?] In DWT based edge detection scheme by scale multiplication, the scale product function of f(x,y) is defined as the correlation of two adjacent DWT scales, Pj

f,1(x, y) = Wj

1f(x,y) * Wj+11f(x, y) and Pj

f,2(x, y) = Wj2f(x,y) * Wj+1

2f(x, y). Subscript j means that the correlation is computed at scales 2j and 2j+1. A single threshold t applied to module map to remove the noise and detect edge points. The parameters tested are j {1, 2, 3, 4, 5}, t {0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0}. [similarly, what do they mean?] SUSAN edge detector allows user to specify two parameters: (1) spatial control for flat mask – either 37 pixels arranged in a “circle”, or a 3 by 3 mask which gives finer detail; (2) brightness control t, determining the maximum difference in greylevels between two pixels which allows them to be considered part of the same ‘region’ in the image. The values of t tested in this study is t {2,3,4,5,6,7,8,9,10}. [need to explain these as well. All these should be in “Method” section]

[do these 4 methods apply to the floating point data array, or the byte-value image? If it is the latter, how are these byte images derived from SST (floating point) and chl (floating point)? Why?]

3.2 SST resultsFour algorithms were tested on the SST image of March 12, 2005 to select

‘optimal’ parameters, again by visual assessment. The ‘optimal’ parameters were those that gave the most and obvious fronts and not introducing significant false and noisy edges. The results are shown in Figure 1. The parameters obtained and used to produce

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the results shown in Figure 1 are as follow: (1) Canny detector, sigma = 1.0, low = 0.05, high = 0.10; (2) histogram algorithm, window size = 32 x 32, (opt) = 0.72, C = 0.92, C1 = C2 = 0.90; (3) DWT scale multiplication, j = 3 (lower scales are sensitive to noise, while higher scales result in poor edge localization), t =1.0; (4) SUSAN algorithm, flat mark = 37 pixels “circle”, t = 2.

As shown in Figure 1, Canny detector, histogram algorithm and DWT scale multiplication give similar performance, and do obviously better than SUSAN detector. Further discussions does not include SUSAN detector. Canny detector seems to give better finer features near the costal area than histogram algorithm and DWT (Figure 1a, 1b, 1c). Front labeled as #1 in Figure 1a is the one detected by Canny and DWT detectors but not by histogram algorithm. Figure 2(1) shows the SST transact across the fronts #1 in Figure 1a, which corresponds to a 0.8 oC temperature gradient across the detected front edge. Fronts labeled as #2 and #3 were detected by Canny but not by histogram algorithm and DWT. Figures 2(2) and 2(3) show the SST transacts across the fronts #2 and #3 in Figure 1a, which correspond to a 0.4 oC temperature gradient across the detected front edges. Fronts labeled as #4 and #5 in Figure 1b are detected by histogram algorithm and DWT but not by Canny. The SST transacts across the fronts #4 and #5 are shown in Figure 2(4) and 2(5), which corresponds to a 0.6 oC and 0.8 oC temperature gradient, respectively. [so?]

The same set of parameters derived from image of March 12, 2005 above is applied to images collected for different dates. Figure 3 shows similar results for SST image of February 11, 2005. All algorithms detect most of the visually obvious strong front edges. Canny performs better in detecting the finer features near the costal area, while fails to detect some relatively strong fronts (i.e. the arrow pointed front edge in Figure 3b). One problem with the Canny operator is to do with Y-junctions i.e. places where three ridges meet in the gradient magnitude image [no clear what this means]. For histogram algorithm, as pointed out in Cayula and Cornillon (1992), the window size must be sufficiently large to obtain reliable statistics; on the other hand, a too large window may include interference features in the area under study. In the original configuration of Cayula and Cornillon (1992), the size of the windows was set to 32 x 32. Our testing also shows that window size of 32 x 32 gives better results than that of 16 x 16 and 64 x 64. However, if there are more than two fronts within a 32 x 32 window, the algorithm will miss the front representing a smaller portion of the window. This might partially explain its failure to detect finer features near the costal area. [this discussion is the best so far as I read]

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#2 #3#1

# 4 # 5

(a) (b)

(c) (d)

Figure 1. SST fields on West Florida Shelf, March 12, 2005, with edges overlaid. (a) edges detected by Canny, (b) edges detected by histogram algorithm, (c) edges detected by DWT, (d) edges detected by SUSAN.

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(1) (2)

(3) (4)

(5)

Figure 2. SST transacts across the fronts labeled in Figure 1a and 1b. Figures (1), (2), (3), (4) and (5) correspond to fronts #1, #2, #3, #4 and #5 in Figure 1a and 1b, respectively.

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(a) (b)

(c) (d)

Figure 3. SST fields on West Florida Shelf, Februry 11, 2005, with edges overlaid. (a) edges detected by Canny, (b) edges detected by histogram algorithm, (c) edges detected by DWT, (d) edges detected by SUSAN.

Again, three algorithms produce similar results on SST image of May 25, 2005, which has quite uniform surface temperature within the study area (Figure 4).

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(a) (b)

(c) (d)

[How are clouds handles? How do they perform on images with lots of scattered clouds? What about coastlines? Can you have some statistics in addition to visual and gradient evaluations, such as total # of frontal pixels from each method? Otherwise the above comparison is really qualitative]

3.3 Chl-a resultsThe same four algorithms were tested on the Chl-a image of March 12, 2005 to

select ‘optimal’ parameters, again by visual assessment. The ‘optimal’ parameters were those that gave the most and obvious fronts and not introducing significant false and

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Figure 4. SST fields on West Florida Shelf, May 25, 2005, with edges overlaid. (a) edges detected by Canny, (b) edges detected by histogram algorithm, (c) edges detected by DWT, (d) edges detected by SUSAN.

noisy edges [how do you know they are obvious fronts or false/noisy fronts? Do you have some quantitative measure?]. The results are shown in Figure 5. The parameters obtained and used to produce the results shown in Figure 5 are as follow: (1) Canny detector, sigma = 1.0, low = 0.1, high = 0.15; (2) histogram algorithm, window size = 32 x 32, (opt) = 0.72, C = 0.92, C1 = C2 = 0.90; (3) DWT scale multiplication, j = 3 (lower scales are sensitive to noise, while higher scales result in poor edge localization), t =1.0; (4) SUSAN algorithm, flat mark = 37 pixels “circle”, t = 5.

Contract to the results of SST images, Canny detector produce quite different results from histogram algorithm and DWT scale multiplication. Again SUSAN detector performs most poorly. Further discussions exclude SUSAN detector. Canny detector gives much better finer features near the costal area than histogram algorithm and DWT (Figure 5a, 5b, 5c). Front labeled as #1 in Figure 5a [I can’t see the front] is detected by Canny but not by histogram algorithm and DWT. Figure 6(1) shows the SST [Chl-a?] transact across the fronts #1 in Figure 5a, which corresponds to a 0.2 Chl-a gradient across the detected front edge. Front labeled as #2 is detected by Canny and DWT but not by histogram algorithm. Figures 6(2) show the Chl-a transact across the fronts #2 in Figure 5a, which correspond to a 0.25 Chl-a gradient across the detected front edge. Canny detector fails to detect low Chl-a fronts in the center of study area. Fronts labeled as #3, #4 and #5 in Figure 5b are detected by histogram algorithm and DWT but not by Canny. Even when the parameters for Canny are lowered to low = 0.01, high = 0.02, Canny still does not detect the edges such as #3, #4 and #4 in Figure 5b. The Chl-a transacts across the fronts #3, #4 and #5 are shown in Figure 6(4) and 6(5), which corresponds to a 0.07, 0.03 and 0.05 chl-a gradient, respectively. The magnitudes of these chl-a gradients correspond to a change of 2-3 pixel values across the detected fronts. DWT algorithm seems to perform best in detecting low chl-a fronts. For example, front labeled as #6 in Figure 5c was not detected by Canny and histogram algorithm. The Chl-a gradient across front #6 is about 0.05 (Figure 6(6)).

The same set of parameters derived from image of March 12, 2005 above is applied to images collected for different dates. Figure 7 shows similar results for chl-a image of April 4, 2005. Canny performs better in detecting the fronts near the costal area, while histogram algorithm and DWT are sensitive enough to detect the low Chl-a fronts in the center of study area.

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#1

# 2

# 3

# 4 # 5

# 6

(a) (b)

(c) (d)

Figure 5. Chl-a fields on West Florida Shelf, March 12, 2005, with edges overlaid. (a) edges detected by Canny, (b) edges detected by histogram algorithm, (c) edges detected by DWT, (d) edges detected by SUSAN.

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(1) (2)

(3) (4)

(5) (6)

Figure 6. Chl-a transacts across the fronts labeled in Figure 5a ,5b and 5c. Figures (1), (2), (3), (4), (5) and (6) correspond to fronts #1, #2, #3, #4, #5 and #6 in Figure 5a, 5b and 5c, respectively.

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(1) (2)

(3) (4)

4. SummaryAll the algorithms evaluated in this study, except SUSAN detector, gives similar

overall performance on SST images [how many images have been evaluated?] off the West Florida Shelf. Canny detector performs better than other detectors in detecting and retaining finer scale [give measure, < 5 km? 10km?] fronts near the coastal area. [what is the “optimal” parameter set for each algorithm?] [Does Canny have some shortcomings?

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Figure 7. Chl-a fields on West Florida Shelf, April 4, 2005, with edges overlaid. (a) edges detected by Canny, (b) edges detected by histogram algorithm, (c) edges detected by DWT, (d) edges detected by SUSAN.

If not this must be the best candidate, right? If yes what are they?]For Chl-a images, Canny detector performs better in detecting edges near the

shore where chl-a has high values (sometimes erratically high), but performs poorly in detecting low chl-a fronts in the center of study area [where is the center?]. On the contrary, histogram algorithm and DWT perform better in low chl-a area than in the high chl-a costal area. [the words “high”, “low” etc. without context are not scientific languages. Need to give some specific numbers, for example, > 5 mg m-3 <0.5 mg m-3 , etc.] [this is very qualitative summary. Give some specifics]

For practical point of view, it is difficult, if not impossible, to select a single algorithm which can provide same high level of performance in detecting various type of fronts, i.e. mesoscale fronts vs fine scale fronts, strong fronts vs weak fronts. Selection of a particular edge detection algorithm for marine satellite images is most likely a task-based choice. [this last paragraph makes the entire paper nearly useless – what can a reader learn from your work? Can you recommend something(s)? For example, under such and such circumstances, use this method with this parameter set…. For SST, do this…. For Chl do that….]

[Visual judgment is not a good evaluation, because color is illusive. I can tweak color to show whatever feature you want to see or not want to see. So, this must be pointed out in the discussion, and most importantly, add some quantitative measure for evaluation, such as your detailed gradient analysis, some statistics on the frontal pixels for large and small features respectively, some more tests on other images where contrast is not as strong as in the given examples, cloud edge problems, etc.]

[Overall this is a good start, but major improvement is needed. If I can summarize: 1. Methods. Describe each method concisely yet let an average reader like me understand the principle, and what those parameters mean (if you change x from 0.5 to 0.6 what does the algorithm do).Results. Have you tested many more images under all circumstances? Is the “optimal” set, chosen from one image only, always “optimal” for all other images? Give quantitative measures – this is importantDiscussion. What do these results mean? What are the remaining issues? If an algorithm miss one front, so what?Summary. Make it a real summary to include some quantitative numbers and some recommendations, so other people don’t need to spend months again trying to find the “best” edge-detection method]

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