Detecting anomalous objects in hyperspectral data using segmentation

Hila Blecher Segev; Stanley R. Rotman; Dan G. Blumberg

doi:10.1117/12.797161

2 October 2008 Detecting anomalous objects in hyperspectral data using segmentation

Hila Blecher Segev, Stanley R. Rotman, Dan G. Blumberg

Proceedings Volume 7113, Electro-Optical and Infrared Systems: Technology and Applications V; 711314 (2008) https://doi.org/10.1117/12.797161
Event: SPIE Security + Defence, 2008, Cardiff, Wales, United Kingdom

Abstract

In this research, our goal is to identify anomalous targets in hyperspectral and multispectral images; our sole starting information is that the targets are larger then a single pixel and their spectral signatures are different than the background. The algorithm is executed as follows: first, we use the Principal Component Analysis (PCA) transformation to find the Projecting the data into this subspace, we create a two-dimensional histogram. From the peaks of this histogram, a set of segments is determined. In comparison, we can alternatively segment our image using the well-known Kmeans approach. We then define the larger clusters to be background segments; each pixel in the image is then given a value based on the minimum "distance" from one of the segment averages. We use three different distance measures: the Mahalanobis distance, the Euclidian distance and the Spectral Angle Mapper (SAM). These dissimilarity measures are used to evaluate the pixels that are extremely different from all of the background clusters. The "anomalies" can be found by thresholding the results. We present the results of a field test using this algorithm; we have succeeded in reaching high detection rates while keeping a very low false alarm rate.

Citation Download Citation

Hila Blecher Segev, Stanley R. Rotman, and Dan G. Blumberg "Detecting anomalous objects in hyperspectral data using segmentation", Proc. SPIE 7113, Electro-Optical and Infrared Systems: Technology and Applications V, 711314 (2 October 2008); https://doi.org/10.1117/12.797161

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available