Effective training set sampling strategy for SVDD anomaly detection in hyperspectral imagery

Date

2014-05-07

Author

Ergul, Mustafa
Sen, Nigar
Okman, O. Erman

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

241
views

0
downloads

Anomaly detection (AD) is an important application for target detection in remotely sensed hyperspectral data. Therefore, variety kinds of methods with different advantages and drawbacks have been proposed for past two decades. Recently, the kernelized support vector data description (SVDD) based anomaly detection approaches has become popular as these methods avoid prior assumptions about the distribution of data and provides better generalization to characterize the background. The global SVDD needs a training set for the background modeling; however, it is sensitive to outliers in the data; so the training set has to be generated with pure background spectra. In general, the training data is selected by random selection of the pixels spectra in entire image. In this study, we propose an approach for better selection of the training data based on principal component analysis (PCA). A valid assumption for remotely sensed images is that the principal components (PCs) with higher variance include substantial amount of background information. For this reason, a subspace composed of several of the highest variance PCs of cluttered data can be defined as background subspace. Thus, with the proposed algorithm, the selection of background pixels is achieved by projecting all pixels in the image into the background subspace and thresholding them with respect to the relative energy on the background subspace. Experimental results verify that the proposed algorithm has promising results in terms of accuracy and speed during the detection of anomalies.

Subject Keywords

Hyperspectral data processing, Target detection, Anomaly detection, Support vector data description, Effective training set generation strategy

URI

https://hdl.handle.net/11511/67304

DOI

https://doi.org/10.1117/12.2051040

Collections

Unclassified, Conference / Seminar

Suggestions

OpenMETU
Core

Fully-Automatic Target Detection and Tracking for Real-Time, Airborne Imaging Applications Alkanat, Tunc; Tunali, Emre; Oz, Sinan (2015-03-14) In this study, an efficient, robust algorithm for automatic target detection and tracking is introduced. Procedure starts with a detection phase. Proposed method uses two alternatives for the detection phase, namely maximally stable extremal regions detector and Canny edge detector. After detection, regions of interest are evaluated and eliminated according to their compactness and effective saliency. The detection process is repeated for a predetermined number of pyramid levels where each level processes a...
A COMPARATIVE STUDY OF HYPERSPECTRAL ANOMALY AND SIGNATURE BASED TARGET DETECTION METHODS FOR OIL SPILLS Soydan, Hilal; Koz, Alper; Düzgün, Hafize Şebnem; Alatan, Abdullah Aydın (2015-06-05) Hyperspectral target detection methods have until now progressed mainly on two paths in remote sensing research. The first approach, anomaly detection methods, use the difference of a local region with respect to its neighborhood to analyze the image without using any prior information of the searched target. The second approach on the other hand uses a previously obtained signature of the target, which uniquely represents the target's characteristics with respect to the spectral wavelengths. The signature ...
Deep convolutional neural networks for airport detection in remote sensing images Budak, Umit; Sengur, Abdulkadir; Halıcı, Uğur (2018-05-05) This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not o...
Radar target detection in non-gaussian clutter Doyuran, Ülkü; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2007) In this study, novel methods for high-resolution radar target detection in non-Gaussian clutter environment are proposed. In solution of the problem, two approaches are used: Non-coherent detection that operates on the envelope-detected signal for thresholding and coherent detection that performs clutter suppression, Doppler processing and thresholding at the same time. The proposed non-coherent detectors, which are designed to operate in non-Gaussian and range-heterogeneous clutter, yield higher performanc...
Robust Automatic Target Recognition in FLIR imagery Soyman, Yusuf (2012-04-24) In this paper, a robust automatic target recognition algorithm in FLIR imagery is proposed. Target is first segmented out from the background using wavelet transform. Segmentation process is accomplished by parametric Gabor wavelet transformation. Invariant features that belong to the target, which is segmented out from the background, are then extracted via moments. Higher-order moments, while providing better quality for identifying the image, are more sensitive to noise. A trade-off study is then perform...

Citation Formats

M. Ergul, N. Sen, and O. E. Okman, “Effective training set sampling strategy for SVDD anomaly detection in hyperspectral imagery,” 2014, vol. 9088, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67304.