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https://github.com/xquery/sdss_asteroid_detect

asteroid detection from Sloan Digital Sky Survey (SDSS) images (ALPHA - unstable under development)
https://github.com/xquery/sdss_asteroid_detect

asteroid detection sdss

Last synced: about 2 months ago
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asteroid detection from Sloan Digital Sky Survey (SDSS) images (ALPHA - unstable under development)

Host: GitHub
URL: https://github.com/xquery/sdss_asteroid_detect
Owner: xquery
License: apache-2.0
Created: 2017-07-05T20:19:41.000Z (over 7 years ago)
Default Branch: develop
Last Pushed: 2017-11-04T12:53:49.000Z (about 7 years ago)
Last Synced: 2024-10-16T12:21:12.308Z (2 months ago)
Topics: asteroid, detection, sdss
Language: CMake
Homepage:
Size: 27.9 MB
Stars: 1
Watchers: 3
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # sdss_asteroid_detect 

[![Build Status](https://travis-ci.org/xquery/sdss_asteroid_detect.svg?branch=master)](https://travis-ci.org/xquery/sdss_asteroid_detect)

IMPORTANT NOTE- this software is unstable and under development.

Small collection of utilities for detecting moving objects within [sdss](http://www.sdss.org/) images.

## Usage

The simplest approach is to grab a jpg image from SDSS and run naive_detect:

```

> naive_detect 

```

which will generate a candidate.jpg if it contains a potential moving object, marking its location

on the jpeg.

The moving object is identified by a circle in the following example image.

![Example candidate image2](data/test/positives/candidate_example2.jpg)

here is another example.

![Example candidate image](data/test/positives/candidate_example.jpg)

here is a good example of detection at the limit of image resolution.

![Example candidate image3](data/test/positives/candidate_example3.jpg)

## Overview

This effort is an attempt to detect moving objects (eg. asteroids) in SDSS images at scale.

* [SDSS - Sloan Digital Sky Survey](https://en.wikipedia.org/wiki/Sloan_Digital_Sky_Survey)

* [Sloan Digital Sky Survey](http://www.sdss.org/)

* [SDSS datasets](https://data.sdss.org/sas/dr13)

* [opencv](https://en.wikipedia.org/wiki/OpenCV)

The way [SDSS captures](http://cas.sdss.org/dr5/en/proj/basic/asteroids/findingasteroids1.asp) image data makes it straightforward to identify moving objects.

The general algorithm for detection I have developed is as follows:

* split image into RGB layers then subtract from each other

* stationary objects should negate themselves

* if there is anything remaining it will indicate offset aka movement

* convert to grayscale for circle detection using [HoughCircle](https://en.wikipedia.org/wiki/Hough_transform)

 

Because it is most familiar to me, I am currently working with jpeg imagas though will be switching over to using FITS soon. 

 

False positives are an issue:

* image flaring

* detecting poor image quality

* unknown (ex. picking up other kinds of artifacts/moving objects in SDSS image)

I have not quite got to 'the at scale' part as I am still grokking lots of ancillary SDSS data.

 

## Next steps

  

* publish results to [sdss asteroid detect s3 bucket](http://sdss.asteroid.detect.s3-website-us-east-1.amazonaws.com/)  

* Layer in ML approaches for better false positive detection 

* Genetic alg for identifying optimal artifact detection parameters

* Reconcile and report to [Moving Object Catelog](https://www.researchgate.net/publication/238534010_The_Sloan_Digital_Sky_Survey_Moving_Object_Catalog)

## Build and deploy

To build you may have to install some additional deps:

```

yum install epel-release

yum install cmake3

```

To build this set of utilities on linux, osx and windows platforms.

```

>cmake3 -DCMAKE_BUILD_TYPE=Debug -G "CodeBlocks - Unix Makefiles"

```

Note that running cmake will pull down dependencies.

Then run make, make install.

To create a release package

```

>cpack3 --config CPackConfig.cmake

```

### Dependencies

This project depends on the following external libs:

* [opencv](https://github.com/opencv/opencv): for image processing

* [rapidjson](https://github.com/miloyip/rapidjson): for json munging

* [loguru](https://github.com/emilk/loguru): for logging

* [gnuplot-cpp](https://github.com/orbitcowboy/gnuplot-cpp): for speaking to gnuplot

* [googletest](https://github.com/google/googletest): for testing

* [curl](https://curl.haxx.se): for http 

## License

[Apache License v2.0](LICENSE)