GG 710S - Remote Sensing in Submarine Environments Lecture 4 Introduction to Sidescan Processing

Transcription

1 GG 710S - Remote Sensing in Submarine Environments Lecture 4 Introduction to Sidescan Processing Scenario: You ve been given a file of sidescan data to process. Where to begin? There are lots of ways to process sidescan data, but for novices, a good beginning is to use a visual processing system. In HMRG-land, we have developed our own tool called ssp (that stands for sidescan processor ). ssp is a graphical tool whose function is to call a bunch of processing programs (any of which can be run separately or in batch mode ) and allow you to set parameters based on visual inspection of their performance with a particular dataset. Assuming you ve set your environment correctly to run the program, ssp can be invoked by simply typing: ssp filename In our case, let s use the test.ss dataset that was provided in class: ssp test.ss After you type this, a new window will appear on your screen. It ll look like this:

2 Brief Description of the Main SSP Display Window This window contains two copies of the same piece of sidescan data (typically one hour file). The upper copy is the reference dataset, and until you exit from ssp and save your edits to the data, it will not change. The upper copy of sidescan data is meant to act as a guide so that you can see what unprocessed data look like. The lower version of the sidescan data is the working copy that will display the changes you apply using different processing algorithms. The data depicted in the lower swath should change. It is up to you to compare the modified data in the lower window with the unprocessed data in the upper window and determine if the parameters you are using have made the data better or worse. In both copies of the sidescan swath, the distance from vehicle nadir (in kilometers) is indicated on the left side of the swath. Zero (0) corresponds to the nadir location, negative values indicate port side data and positive values indicate starboard side data. This means that time is increasing along the y-axis, so data collected at the left-hand end of the swaths were collected prior to data at the right-hand end. On the left side of the window there should always be tick marks along the distance axes that provide finer scale subdivisions for your reference. Within a sidescan swath there are typically a few hundred pings which, like time, increase in number from left to right. Within each ping are many sidescan amplitudes that we call samples. Sample 1 is the closest sidescan value to nadir on both port and starboard sides. Many of the HMRG processing programs refer to ping neighborhood, which refers to adjacent values in the left-to-right sense and sample neighborhood, which indicates adjacent values in the up-and-down sense within the displayed swath. Near the top of the SSP Display window are several features to help you display the data in different ways. You should use these tools to optimize the display for features you are trying to examine (and probably remove): o The - + in the upper left-hand corner are for quick zoom-in and zoom-out capability. Click + with the left mouse button zoom in; to zoom out, click -. o To show Out of Bounds data (values too high or too low), click on Avg with the left button. OOB should be display. When you don t want to see out-of-bounds data values, make sure Avg (average) is displayed. o To switch between three different contrast enhancements, click the left mouse button on the HstEq and see which graymap, (histogram equalized, linear or logarithmic) works best for your data. o To change the high and low values used for the graymap, click on the numbers on either side of the color (gray) bar. Clicking with the left mouse button will make the numbers decrease, while clicking with the middle mouse button will make them increase. o To reverse polarity on the graymap (from high=white and low=black to high=black and low=white), click on the color bar. o If you need to know a specific ping number (or ping time), use the cursor to move your mouse so that it is pointing directly over the ping of interest. The ping information is displayed in the upper right-hand corner of the ssp window. For example: Ping 333 at 1999/104 23:52:37 Changing SSP Attributes Alternatively, you can change the display attributes for your data using the pull-down menu system. This is accomplished by holding down the right mouse button anywhere in the ssp window. Holding down the right mouse button is also how you access all of the processing

3 programs. Use of the right mouse button should make a small new window pop up inside of the main window. If you continue to hold down the right mouse and slide up and down the text in this smaller window, you will see the background of the text directly under the cursor be highlighted. Sometimes an additional pull-down menu will appear: Try highlighting Attributes -> Edit (move the cursor over Edit so that both words have highlighted backgrounds) and release the mouse button. A new window will appear: The SSP Attributes window controls the display settings in the same ways as described for the features at the top of the main SSP Display window. It also allows you to do more. For example, try clicking on the Display Range button for Port and replacing the 1 in the Ping Width box with 2 to get a new display. Be sure to click the Apply button when you have made the changes (or Cancel if you ve made a mistake or changed your mind about

4 altering the display attributes). As soon as you click the Apply button the SSP Attributes window will disappear, and the SSP Display will refresh with a new view of your data: Now it s a lot easier to see the noise in the portside sidescan dataset. Some Types of Noise and Their Causes There are several types of noise visible in the sidescan data. There are white stripes that run parallel to vehicle nadir. We call these track parallel stripes. They are caused by interference from a system that is synchronized with the seafloor mapping sonar. You will use the program bsdetpstripe to remove these features. There are also track-perpendicular white lines, some nearly as wide as a full ping and some only slightly longer in the track-perpendicular direction than in the track-parallel direction. The latter are caused by interference from another acoustic source, in this case one this has not been synchronized with the sidescan sonar. In HMRG-speak these are stripes (full ping) or microstripes (partial ping) and will be removed using the programs bsdestripe and bsdemicrostripe, respectively. Finally, if we zoom in very close to the data (see below) it appears that very fine-grained salt and pepper have been sprinkled all over the data. This salt-and-pepper noise is called speckle, and it is caused by constructive and destructive interference of sound waves bouncing off scattering surfaces on the seafloor. It can be removed using a program called bsdespeckle.

5 There are other types of noise that we will discuss in future processing sessions, but let s do some experimenting with track-perpendicular and track-parallel stripes plus speckle to see how we can improve our data and get used to using the visual approach to editing sidescan data. First, let s learn how to access various data processing algorithms. The processing programs are accessed via the pull-down menu system, so you must click and hold the right mouse button in the SSP Display window until the text Process is highlighted. As with the Attributes selection, you will see a nested pull-down menu appear that has a list of processing approaches you can apply to your dataset. This pull-down text list also has a dashed line across its top, which means it is a tear-off window; that is, if you click on the dashed line using the right mouse button, the list of processing programs will appear in a separate window that doesn t close. That gives you instant access to these algorithms without repeatedly having to use the pull-down menu (a chore that can be rather difficult when you are bouncing around on the ocean). Examples of pull-down and tear-off window access to the processing algorithms are shown below. Note that you can use either approach and get the same result for your data, it s just more convenient to use the tear-off windows when you are processing a lot of data or working in a dynamic environment.

6 ssp display with pull-down menu for bsdestripe: ssp display with tear-off menu for bsdestripe:

7 Removing Track-Perpendicular Stripes As a first experiment, let s try removing a track-perpendicular stripe. These are pings that run the entire width of the swath (or close to it) and have anomalously high or low values compared to adjacent pings. Track-perpendicular stripes usually occur in a couple of ways: o Sonar electronics create these artifacts through system crashes, changes in system operating parameters applied during a ping, etc. Track-perpendicular stripes caused by the sonar electronics typically have no useful data and should be removed. o Sudden motion of the towfish can reduce signal strength significantly. For example, if the ship pulling the fish is running through heavy seas and a large shudder caused by crashing into a wave is translated down the wire, the fish may suddenly pitch sharply, so that sound isn t directed straight down, but well in front of or behind the towfish instead. In this case boosting the strength of the recorded signal to the same levels as the pings around it will preserve relative values acquired by the sonar. The algorithm for detecting and fixing track-perpendicular stripes is very simple: add all of the sidescan values within a ping and compare it to the summed values of pings on either side (note that port and starboard side data are treated separately). If the resulting sum is above or below a certain threshold, then either create a new ping by interpolating from one side to the other, or boost the intensity of the ping by normalizing it relative to the summed totals for adjacent pings. As the data processor, you can use the Destripe: menu to optimize settings for removing bad pings. First, highlight the word Destripe in either the pull-down or tear-off text lists. When you move the cursor out of the menu you are using, you ll see a change in the cursor s shape (it s gone from an arrow to a cross). This signifies that you now need to select the data region on which you d like to operate. Remember, we are only trying to determine the best settings for removing track-perpendicular stripes at this point, not process the whole dataset, so just find a stripe you don t like and click the left mouse button a few pings to either side of it. After the second mouse click a new window will appear:

8 You don t need to worry about the boxes at the top of this window. The Path Prefix and Directory are for any software developer who might be working on improvements to the existing algorithm and have another version of the code in a different directory. When data volumes are low or data are clean, processing is faster if you mark only pings that need processing and then run the processing algorithms on just these pings. For the example datasets you ve been given in class Ping Marks should always be set to Ignore. When data have a large dynamic range (values going from near zero into the millions) it is best to process them using histogram equalization. For systems with limited dynamic range (i.e., 0-255), the linear summation mode works best. Bottom line: for the data you are using, don t modify anything above the Tolerance. That said, clicking on the Help button in the upper right-hand corner is a useful thing to do it will display the manual page for the Destripe program. All of the HMRG software processing modules allow access to their manual pages in this way. As mentioned in the algorithm description, the summed ping values are compared to sums from neighboring pings, and the algorithm replaces pings that fall outside of some tolerance. This tolerance can be set so that high- and low-end members are treated the same way ( Uniform tolerance) or so that high and low discrepancies are treated differently ( Directional tolerance). For example, you might want to find any pings more than 10% different from their neighbors regardless of whether they are too high or low, or you may have noticed that all of the noise in your files has high amplitude, and so you set the low tolerance to be a large number (this will prevent anomalously low pings from being found) and the high tolerance to be a lower number, perhaps 30%. If the Ping Neighborhood Radius stays 4 (pings on either side of the THIS ping), the program removes anomalously high pings that fall across most of the swath:

9 Removing Track-Parallel Stripes Track-parallel stripes are unique to platforms that have a couple of synchronized sonars that operate at almost the same frequency (or record data over a broad band of frequencies). In the case of the SCICEX program the swath mapping and chirp subbottom systems interfered with each other. The subbottom sonar frequently changed the delay between when pings started, so in SCICEX sidescan data the track-parallel stripes move closer and farther from nadir at random intervals. Unfortunately, this means the data need to be processed by hand, which is a very timeconsuming process. However, when you are trying to generate a figure for publication, it s important to remove as many artifacts as possible so that reviewers don t get distracted by noise when you are trying to show them interpretations of signal. That is, sometimes we just have to bite the bullet and edit data by hand. To remove track-parallel stripes, highlight the Detpstripe text in the pull-down or tear-off menu and move the cursor into the main SSP Display window. This time, hold down the shift key and click with the left mouse button to signify that you want to process all of the data in the file. The Detpstripe menu will appear:

10 Track-parallel stripes tend to be mirrored about nadir, so set the Stripe Side box to Both. Let s focus on the large stripe ~2100m from nadir. The Stripe Width (in meters) can be visually estimated by zooming into the data with the + button. Neighborhood Width is the width in meters of a neighborhood on either side of the stripe whose data values will be used to help locate the stripe. If you leave it set to Stripe Width and leave Stripe Width at the default of 90 meters, the program will examine data and meters from nadir on port and starboard sides to try and find a stripe. Analysis Mode should be set to Histogram Equalize since these data have large dynamic range. The Amplitude Ratio is analogous to the Tolerance for track-perpendicular stripes a ratio of 1.1 means if the program finds a stripe that is 110% (or 10% higher) than its neighborhood average, it should be replaced. The Minimum Stripe Overlap Factor specifies the straightness of a stripe: 1 indicates the stripe is perfectly parallel to nadir, 0 indicates that the stripe can wander up and down from ping to ping. In track-parallel stripes noise swamps the signal, so there s no hope of recovering real data. Instead, we use an old NASA technique and introduce noise (actually data values copied at random from around the bad stripe) to make the track-parallel artifact disappear. How randomly these values are selected is determined by the Randomization Factor. Let s try an example: set Amplitude Ratio to 1.01 and the Minimum Stripe Overlap Factor to 0.8. The Randomization Factor can stay at the default setting of 0.2. With Stripe Location at 2100 and Stripe Width at 90 we get the following change: Arrows show location of stripes in the raw data that are gone in the Working Copy.

11 Removing Speckle Speckle, or salt-and-pepper noise, is something that should be found in every sidescan dataset since sound waves reflecting from seafloor scatters must interfere constructively or destructively just like ripples in a puddle during a rainstorm. The approach for removing speckle noise is to median filter the dataset. This is accomplished by running a boxcar filter over neighboring pings and samples, sorting the values, discarding the upper and lower end members of the sorted list, and then comparing the value in the center of the boxcar with the average of the remaining values in the sorted list. If the central value is lower or higher than a specified tolerance about the average, it will be replaced by the middle value of the sorted list. As with track-parallel stripes, the despeckling algorithm should be run on an entire dataset in the same way, so use the pull-down/tear-off menus to highlight Despeckle and then move the cross-shaped cursor to the main SSP Display window and hold down the shift button while you click with the left mouse button. The Despeckle window will appear: As with Destripe and Detpstripe you can ignore the Module parameters. Focus your attention on changing values from the Boxcar Width line to the bottom of the window. As we learned with the navigation smoothing experiment (Lecture 02), the larger filter size the more a median filter will smooth (blur) your data, so it s usually good to keep Boxcar Width and Boxcar Height set to 3 to preserve the maximum amount of original signal. The Discard Percentage tells the program how much of the sorted list to ignore. This value can be set to 0%

12 (i.e., don t ignore any values); the default of 20% for a 3x3 boxcar will discard 10% (one value) at the top and bottom end of the sorted list. Tolerance for bsdespeckle is identical to that described for bsdestripe (described earlier). Data Replacement can either highlight the values that will be removed or replace them with the median. Let s leave the boxcar width and height at 3, leave the Discard Percentage at the default value of 20, use a Directional Tolerance with the Low Percentage set to a very high number ( ) and the High Percentage set to 150. If the program is executed with the Data Replacement mode set to Highlight the following change results: In this example, green (out-of-bounds for anomalously high) values are those that will be replaced if the program is run with the same parameters but turning on Median for Data Replacement instead of Highlight. NOTE that whenever you use the Highlight mode, it is important to Undo the operation before re-running the program in a different mode. The Undo command can be accessed using the pull-down and tear-off menus, but it s much easier to simply type the letter u in the main SSP Display window. Similarly to Redo an operation, type r in the main SSP Display window and the program module (i.e., Despeckle or Detpstripe, etc.) should appear. Since there s not much green in the image above, let s decrease the High Percentage to 100 and set the Data Replacement to Median. The resulting figure has a lot less speckle noise than the raw data (further improvements are left as an exercise for the reader!):

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