This site is deprecated. See the front page for more information.
Child pages
  • GSoC Project Extension
Skip to end of metadata
Go to start of metadata

This extension is a project of Google Summer of Code 2010 for Opticks. The extension contains target detection, anomaly detection and data merge tools.


Dalong Cheng

Latest Version1.0
Updated On




Supported Opticks Releases: 


Supported Operating Systems: 

Windows 32-bit
Windows 64-bit
Solaris SPARC 64-bit
Linux 64-bit

Support Requests:


Data Merge

This feature will help user to merge hyperspectral data from separate files. In this plugin, you could merge several one-band spectral data into a single multi-band spectral data. In order to merge the data, you firstly need to import the data through Opticks, the plugin will automatically list the imported data and let user to choose which of them should be merged. Be aware that all the merged data should have same row and column number. The user could also adjust the band order of
merged data through "up" and "down" button in the GUI.

Target Detection

The target detection will firstly define a concept to measure the similarities between target spectrum and background spectrum, then it will calculate each pixel. In this extention, I implement Adaptive Cosine Estimator(ACE) algorithm for target detection. Fisrtly, you should pick up a pixel in the Spectral image as target spectrum,
then the ACE will take this pixel as target and compare with all the pixel in the Spectral image. The result will be represented in a binary image.  

Anomaly Detection

The anomaly detection algorithn will calculate band data of each pixel with its round pixels. If the difference above certain threshold, the pixel will be treated as a nomaly pixel, since band value it is not comform with its background. In this extention, I use Reed-Xiaoli algorithm for anomaly detection. Due to the large computation of the algorithm, the RX can only be applied to small data spectral data set. Just like ACE algorithm, the RX result will be displayed as a binary image.


Please refer to [1] and [2] for detail mathematics principles of RX and ACE algorithm.
[1] I.S. Reed and X. Yu, "Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution," IEEE Trans. Acoustics, Speech, Signal Processing, vol. 38, pp. 1760-1770, Oct. 1990.
[2] S. Kraut and L.L. Scharf, "The CFAR Adaptive Subspace Detector Is a Scale-Invariant GLRT," IEEE Trans. Signal Process. 47 (9), 1999, pp. 2538--2541

Screenshots and Videos

  Figure 1 ACE test Image, I mark a red circle on the target I choose       


                Figure 2 The binary image of ACE result

          Figure 3 RX test image


  Figure 4 the binary image of RX result

           Figure 5 The GUI of Data Merge Project


All Download

All source code are available at