Showing papers by "Heesung Kwon published in 2003"

Adaptive anomaly detection using subspace separation for hyperspectral imagery

Abstract: We propose adaptive anomaly detectors that find materials whose spectral characteristics are substantially different from those of the neighboring materials. Target spectral vectors are assumed to have different statistical characteristics from the background vectors. We use a dual rectangular window that separates the local area into two regions—the inner window region (IWR) and outer window region (OWR). The statistical spectral differences between the IWR and OWR are exploited by generating subspace projection vectors onto which the IWR and OWR vectors are projected. Anomalies are detected if the pro- jection separation between the IWR and OWR vectors is greater than a predefined threshold. Four different methods are used to produce the subspace projection vectors. The four proposed anomaly detectors are applied to Hyperspectral Digital Imagery Collection Experiment (HY- DICE) images and the detection performance for each method is evaluated. © 2003 Society of Photo-Optical Instrumentation Engineers.

Dual-window-based anomaly detection for hyperspectral imagery

Abstract: We propose adaptive anomaly detectors that find materials whose spectral characteristics are substantially different from those of the neighboring materials.The target spectral vectors are assumed to have different statistical characteristics from the background vectors. In order to detect anomalies we use a dual rectangular window that separates the local area into two regions-- the inner window region (IWR) and outer window region (OWR). The statistical spectral differences between the IWR and OWR is exploited by generating subspace projection vectors onto which the IWR and OWR vectors are projected. Anomalies are detected if the projection separation between the IWR and OWR vectors is greater than a predefined threshold. Four different methods are used to produce the subspace projection vectors. The four proposed anomaly detectors have been applied to HYDICE (HYperspectral Digital Imagery Collection Experiment)images and the detection performance for each method has been evaluated.

Projection-based adaptive anomaly detection for hyperspectral imagery

Abstract: Adaptive anomaly detectors that find any materials whose spectral characteristics are out of context with those of the neighboring materials are proposed. We use a dual rectangular window that separates the local area into two regions- the inner window region (IWR) and outer window region (OWR). The statistical differences between the IWR and OWR is exploited by generating projection vectors onto which the IWR and OWR vectors are projected. Anomalies are detected if the projection separation between the IWR and OWR vectors is greater than a predefined threshold. Four different methods are used to produce the projection vectors. The proposed anomaly detectors have been applied to HYDICE (HYper-spectral Digital Imagery Collection Experiment) images and detection performance for each method has been measured.

Adaptive Target Detection in FLIR Imagery Using the Eigenspace Separation Transform and Principal Component Analysis

Abstract: In this paper, an adaptive target detection algorithm for FLIR imagery is proposed that is based on measuring differences between structural information within a target and its surrounding background. At each pixel in the image a dual window is opened where the inner window (inner image vector) represents a possible target signature and the outer window (consisting of a number of outer image vectors) represents the surrounding scene. These image vectors are preprocessed by two directional highpass filters to obtain the corresponding image edge vectors. The target detection problem is formulated as a statistical hypotheses testing problem by mapping these image edge vectors into two transformations, P 1 and P 2 , via Eigenspace Separation Transform (EST) and Principal Component Analysis (PCA). The first transformation P 1 is a function of the inner image edge vector. The second transformation P 2 is a function of both the inner and outer image edge vectors. For the hypothesis H 1 (target): the difference of the two functions is small. For the hypothesis H 0 (clutter): the difference of the two functions is large. Results of testing the proposed target detection algorithm on two large FLIR image databases are presented.