Streak detection Python package, based on the Fast Radon Transform (FRT)

Written by Guy Nir ([email protected]) Based on the methods described in Nir et al. 2018, Astronomical Journal, vol 156, number 5, (https://ui.adsabs.harvard.edu/abs/2018AJ....156..229N/abstract)

ORIENTATION:

This package includes three classes and a function (and some utilities). The function is frt(), which performs a Fast Radon Transform (FRT) on some data. This function can be used as-is to do transformations, for streak detection or other uses.

To do more advanced searches more code is needed: The three classes included in this package are:

1. Finder: this object contains useful information such as a Radon variance map, needed for normalizing the Radon images and checking Radon peaks against a S/N threshold. Also, the finder can be setup to find short streaks and multiple streaks (that the frt() function cannot accomplish alone). The finder can also convolve the images with a given PSF; it can scan multiple thresholds, effectively removing all point sources; it can section large images into smaller ones to save time and memory; and it can store the resulting streaks and the S/N values for all images which is useful for finding the best threshold and estimating the false alarm rates. The results of all streaks that are found when using the Finder are saved as a list of Streak objects inside Finder.
2. Streak: this object keeps track of the raw Radon coordinates where the streak was found, and also calculates the streak coordinates in the input image, along with other parameters of the streak. Each Streak object keeps a single streak's data, and can be used to display the streak in the original image, to display some statistics on it, and to subtract itself from the image. Streak objects are only created by the Finder object.
3. Simulator: this object can generate images with streaks in them, with a given PSF width and noise, and can automatically feed the resulting images into the Finder it contains. The simulator can make multiple streaks of different intensities and coordinates, and can simulate random streaks with parameters chosen uniformly in a user-defined range.

Using the Finder

To use the full power of the Finder object, use the input() function, giving it three parameters:

from src.finder import Finder
f = Finder()
f.input(im=image_array, psf=psf_array_or_scalar, variance=var_array_or_scalar)

The image should be a numpy array with two dimensions.

The PSF is given either as a scalar, representing the "sigma" coefficient of a Gaussian. This is a useful approximation in many cases. If the FWHM of the seeing is known, it should be divided by 2.355. If the actual PSF is known, it should be input as a small 2D numpy array.

The variance can be given as a scalar which represents the average variance of the image given to the finder, or it can be given as a 2D map of the same size as the image. If the mean noise standard deviation is known (or background noise image) the input to the finder should be the square of these values. The variance can be different for each image, or constant for a series of images given in turn to the finder, that will just re-use the existing variance values if none are given. The default variance values is 1, which assumes the image is normalized.

The variance map, even if it were given as a mean value which is replicated into a uniform map of the same size as the image, is necessary to normalize streaks of different length. For example, even if the variance is set to the default scalar value, the signal for longer streaks will be divided by a larger factor than the signal from shorter streaks. This is achieved by saving a Radon image of the variance map, which is divided by the Radon image of the streak images. These variance Radon maps are cached by the Finder to be re-used with multiple images if needed, and are refreshed when new variance values are given.

Preprocessing input images

The Radon method is very sensitive to any positive bias in the image. Diffuse sources, such as galaxies or even ghosts or reflections can be mis-identified as streaks. Stars, if too many of them are present in the image, may be detected as streaks if they are aligned in a row. Sometimes even two bright stars will trigger the algorithm. On the other hand, any negative bias can reduce the detection efficiency. Since the algorithm sums together many pixels into streaks, even a small bias value can substantially affect the results.

So, an effort should be made to remove stars, and any residual bias from the image. If possible, finding and removing point sources is a useful first step. Measuring the background in a robust way and reducing it is also a good idea (e.g., using sigma clipping, measuring the b/g on different parts of the image individually, etc.). It is up to the user to provide images as clean as possible, before using the tools in this package.

The Finder object does have some ability to remove bias and unwanted sources. First, it runs iteratively, and in each iteration it can remove the bias by subtracting the image mean before finding each streak. Streaks that are found are removed from the image and replaced with NaNs, so they do not count when calculating the mean.

A second mitigation strategy to remove point sources uses multiple iterations of the algorithm with different thresholds, starting with a very high threshold and dividing by two each time. For each threshold the Finder reduces any pixels above the threshold (clips them to the threshold), and then finds streaks. Since point sources have only a few pixels, their signal is never high enough to trigger once they have been clipped. On the other hand, streaks have multiple pixels, each below the threshold, but when summed have a high enough signal to trigger. This can be useful for removing small artefacts and individual stars, but will not guarantee that multiple stars do not trigger together.

If other artefacts and extended sources still remain, they will generally be identified as streaks, but will usually not stop the iterative process from finding actual streaks (from satellites and asteroids). Therefore, some additional work may be needed to classify the different objects identified by the algorithm.

Clipping and sectioning

One option of the Finder is to clip and section the input image before looking for streaks. The clipping option is useful if the image edges contain many artefacts or bias rows. Another reason is to clip a small part of the image so it fits into an integer power of 2. For example if the image is 1050x1050 pixels, the algorithm must zero-pad it to 2048 pixels on one axis, to be able to run the FRT. To save calculating all those empty pixels, clipping the image to 1024x1024 will save calculation time, while only forfeiting a small part of the image.

Sectioning can be used to carve the image into small portions. While the runtime of the algorithm is not very different when running on many subsections instead of once on a large image (the algorithm scales as N*log(N) where N is the total number of pixels), there are some benefits to this mode: (a) very large images can exhaust the system memory, causing significant slowdowns; (b) if one section contains many streaks/artefacts, only that section will require many iterations and many threshold levels; (c) preprocessing can be more accurate for smaller images, e.g., the mean of each section can be measured more robustly. The main downside to sectioning is that very long streaks are either not detected (since not all pixels can be summed at once) and the ones that are found would be split into smaller streak objects. If the section size is larger than the streak the user is interested in, then sectioning can be safely used. For example asteroid streaks tend to be few-pixels long, while LEO satellites can cross the entire image. If looking only for asteroids, sectioning could be useful.

The Finder parameters to control clipping and sectioning are:

• use_sections: enable or disable all clipping and sectioning
• size_sections: the size of the sections to be used
• offset_sections: the offset between lower-left corner of first section and the lower-left corner of the image.
• pad_sections: If True, will add zero padding to sections that extend beyond the image. If False, will clip parts not included in the sections, and drop the sections that are not completely ovelapping with the image.

To generate an image with a single section, but slightly smaller than the original image (i.e., clipping) set a small positive offset and a sections size slightly smaller than the image size:

# assume f is a Finder with a 1048x1048 image
f.use_sections = True
f.size_sections = 1024
f.offset_sections = (12, 12)
f.pad_sections = False

In this case the section starts 12 pixels from the corner, and ends 12 pixels from the other corner, clipping the image to 1024x1024 pixels. If using size_sections=512, the clipping would be the same, but there would be four sections, each with a size of 512x512 pixels.

If the image is slightly smaller than a power of two, padding can be used to pad the sections before starting the analysis:

# assume f is a Finder with a 1000x1000 image
f.use_sections = True
f.size_sections = 1024
f.offset_sections = None
f.pad_sections = True

In this case the image is padded to 1024x1024 pixels, with a single section. If size_sections=512, the padding would be the same, but there would be four sections, each with a size of 512x512 pixels. If pad_sections=False, three of the four sections would be clipped, which would throw away almost 3/4 of the image. Note that even if the sections are not powers of two, they will be padded by the FRT() function.

Using parts of the finding algorithm

The full Finder algorithm requires multiple iterations and can be quite slow, especially if many streaks (or artefacts) are found. To speed things up, in particular if the images are very clean, one can use only a subset of the algorithm.

The full algorithm can be summarized:

1. Cut or clip the image into sections (this is done in the main function input()).
2. For each section, subtract the mean and filter with the image PSF (using preprocess())
3. Choose the highest threshold level for each section, iterate over each threshold until reaching the minimal threshold, while clipping pixels above the threshold (this is done using scan_thresholds())
4. For each threshold, detect multiple streaks on that section and the transpose of that section (this is done using find_multi()).
5. Each time a streak is found using a single call to the FRT() function, on a section or its transpose (this is done using find_single()).
6. Each streak found after the call to find_single() is removed from the image section and replaced with NaNs. If no streaks are found, the iteration stops and the next threshold is scanned.
7. All thresholds must be scanned, with a minimum of two calls to the FRT() function (one for each transpose). If streaks or artefacts are found, the FRT() may be called more times. Use self.data._num_frt_calls to keep track of the number of calls. This counter is reset when calling self.clear().

In some cases, it is faster to skip the scanning of thresholds, particularly if the images do not have any point sources. In that case just running preprocess() and then find_multi() is sufficient. This function will make sure to use the image and its transpose when searching, which is important for finding streaks at all possible angles (a single FRT can only find streaks in the -45 to 45 degree range).

If the image is known to contain only a single streak at most, then running preprocess() and then find_single() twice on the image, once with transpose=False and once with transpose=True. The Finder will find the brightest streak in the image and in the transposed image, by comparing the Radon image to the Radon variance map.

In all the above cases, the preprocess() function requires that the Finder is supplied an estimate of the PSF. This can be given as an array or a scalar width parameter. Either one can be given to finder.data.psf. The Finder also needs an estimate of the variance in the image to properly estimate the S/N of the streaks. Supply the scalar variance value or a variance map of the same size as the image to finder.data.variance.

examples:

Find the brightest streak in an image:

f = Finder()
f.data.psf = psf_width_or_small_array
f.data.variance = variance_mean_or_array_of_same_size_as_image
f.data.image = image
f.preprocess()
s1 = f.find_single(transpose=False)
s2 = f.find_single(transpose=True)

# choose the streak with the best S/N
if s1 is not None and s2 is not None:
   s = s1 if s1.snr > s2.snr else s2
elif s1 is not None or s2 is not None:
    s = s1 or s2
else:
    s = None

if s is None:
    print('No streaks were detected!')
else:
    s.print()  # print the streak coordinates and properties

Find up to 10 streaks in the image and up to 10 streaks in the image transposed:

# assume f already has a preprocessed image, a PSF and a variance:
f.pars.num_iterations = 10  # or give num_iter directly
f.find_multi(num_iter=10)  # if num_iter=None, will use the default given above

for s in f.streaks:  # can be up to 20 streaks
    s.print()  # print the streak details for each streak

For scanning multiple thresholds, and finding multiple streaks at each threshold:

f.pars.threshold = 7.5  # or give threshold directly as "min_threshold"
f.scan_thresholds(min_threshold=None)
# maximum threshold is 7.5 times 2^N,
# where N is big enough to not clip any pixels

# a list of streaks accumulated
# over all thresholds/iterations/transposes:
f.streaks

If there is no need for streak detection, calculation of streak coordinates and S/N, then a single call to FRT() may be sufficient. In this case, the Finder will not be used at all. The output of the function is the raw Radon image, that can still be used to find streaks by searching for hotspots in the transformed image. However, caution should be used as some areas of the raw Radon image have higher signal just because they represent longer streaks, with more pixels and more accumulated noise. To normalize this result, the Radon variance must be used.

Fast Radon Transform function details

The Fast Radon Transform function will calculate the integral of different lines across the image. The output maps an image in x and y into an image in x0 and dx, meaning the offset between the pixel where the integration starts at the bottom of the image and the number of pixels the endpoint is offset from the starting point (i.e., how many lateral pixels does the line traverse when crossing the image from bottom to top). This is limited to the range -45 <= dx <= 45 degrees, so that integrating lines outside that range is acheived by transposing the image and running another transform. To make the transpose, simply supply transpose=True to the FRT function. This applies the transform inside the function.

Because the FRT works by combining rows together, the data must be an integer power of 2. If using padding=True there will be zeros added to the y-axis to pad it to the nearest power of 2. If the image is very far from a power of 2, consider clipping the image down to a power of 2 before feeding it to the FRT function. We always use padding=True because the function will fail if fed a non integer power of 2.

Another limit of the FRT is that streaks that are "corner crossing" will generally fall outside the limit of the transform because their starting point x0 is outside the image axis. To overcome this, the input expand=True will zero pad the x-axis of the image by the height of the image such that every starting point within a 45 degree angle in either direction is included.

Another way to avoid expanding the x-axis is to search for short streaks. This, of course, has many other advantages since many science cases are more interested in short streaks. Since short streaks can be detected even when they are corner crossing, there is no need to use expand. To find short streaks we use partial=True, which saves and outputs "partial Radon transforms". These would be lists of 3D array, where each array represents a different stage in the transform, e.g., the first array for combination of two rows, the next list for combinations of four rows, etc. By finding peaks in a specific 3D array, the user can figure out the length of the streak by knowing how many rows have been "transformed" into that array. For example the third array is for streaks with a length of 8 pixels along the y-axis (the actual length is determined by reading off the dx value as well). Since calculating the S/N for streaks requires also keeping track of partial Radon transforms of the variance maps and matching them with the partial transforms of the sky images, it is recommended that such use cases make use of the Finder class. If partial=False the algorithm will not output the partial transforms, but instead pick only the last 3D array, which has a singleton 2nd dimension, will remove that dimension, such that dx is now the first dimension, and the output will be a 2D image in dx and x0.

Finally, the output argument can be used to give an existing array to the function, which will write into that array instead of re-allocating the output. This is only used for saving runtime. If using partial=True, the output argument should be a list of 3D arrays of the right shapes to hold the outputs.

Reading the results from Streak objects

Cleaning streaks from images

Using the Simulator

NOTES

• this package is based on the MATLAB package (that has some additional GUI functionality but is otherwise less updated) Available on github: https://github.com/guynir42/radon
• Please make sure to cite Nir et al. 2018 (https://ui.adsabs.harvard.edu/abs/2018AJ....156..229N/abstract) if making use of this software.
• use the jupyter notebook "demo.ipynb" to see some examples.