A morphology-based algorithm has been developed for point target detection in IRST applications. It exhibits comparable detection and false alarm performance to a median filter. The morphology-based algorithm has an efficient computational paradigm based on combinations of simple nonlinear grayscale operations, which makes it ideally suited to real- time, high data rate IRST applications. A detection filter based on morphological background estimation exhibits spatial high-pass characteristics emphasizing target-like peaks in the data and suppressing all other clutter. Example cases are presented which point out the detection performance differences between the morphological and median approaches. Overall performance results were generated in the form of ROC curves for cloud, horizon and sea clutter IRAMMP backgrounds.

Morphology-based algorithm for point target detection in infrared backgrounds

Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track

This work studies the performance of dimensional least mean square (TDLMS) adaptive filters as prewhitening filters for the detection of small objects in image data. The object of interest is assumed to have a very small spatial spread and is obscured by correlated clutter of much larger spatial extent. The correlated clutter is predicted and subtracted from the input signal, leaving components of the spatially small signal in the residual output. The receiver operating characteristics of a detection system augmented by a TDLMS prewhitening filter are plotted using Monte-Carlo techniques. It is shown that such a detector has better operating characteristics than a conventional matched filter in the presence of correlated clutter. For very low signal-to-background ratios, TDLMS-based detection systems show a considerable reduction in the number of false alarms. The output energy in both the residual and prediction channels of such filters is shown to be dependent on the correlation length of the various components in the input signal. False alarm reduction and detection gains obtained by using this detection scheme on thermal infrared sensor data with known object positions is presented. >

Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data

Robust detection of small targets is very important in IRST (Infrared Search and Track) This paper presents a novel mathematical method for the incoming target detection problem in cluttered background motivated from the robust properties of human visual system (HVS) The HVS shows the best efficiency and robustness for an object detection task The robust properties of the HVS are contrast mechanism, multi-resolution representation, size adaptation, and pop-out phenomena Based on these facts, a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and Tune-Max based optimization method Simultaneous target signal enhancement and background clutter suppression is achieved by tuning and maximizing the signal-to-clutter ratio (TMSCR) in Laplacian scale-space At the first stage, the Tune-Max of the signal to background contrast produces candidate targets with adapted scale At the second stage, the Tune-Max of the signal-to-clutter ratio (SCR) produces maximal SCR which is used to pop-out detections Experimental evaluation results for the incoming target sequence validate the upgraded detection capability of the proposed method compared with the Top-hat method at the same false alarm rate Experimental results for the six kinds of cluttered background images show that the proposed TMSCR produces less false alarms (43 times reduction) compared to the Top-hat at the same detection rate

Small Target Detection Utilizing Robust Methods of the Human Visual System for IRST

Robust detection of small targets is very important in IRST (Infrared Search and Track). This paper presents a novel mathematical method for the incoming target detection problem in cluttered background motivated from the robust properties of human visual system (HVS). The HVS shows the best efficiency and robustness for an object detection task. The robust properties of the HVS are contrast mechanism, multi-resolution representation, size adaptation, and pop-out phenomena. Based on these facts, a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and Tune-Max based optimization method. Simultaneous target signal enhancement and background clutter suppression is achieved by tuning and maximizing the signal-to-clutter ratio (TMSCR) in Laplacian scale-space. At the first stage, the Tune-Max of the signal to background contrast produces candidate targets with adapted scale. At the second stage, the Tune-Max of the signal-to-clutter ratio (SCR) produces maximal SCR which is used to pop-out detections. Experimental evaluation results for the incoming target sequence validate the upgraded detection capability of the proposed method compared with the Top-hat method at the same false alarm rate. Experimental results for the six kinds of cluttered background images show that the proposed TMSCR produces less false alarms (4.3 times reduction) compared to the Top-hat at the same detection rate.

A new least-mean-square filter technique is defined for signal detection problems. The technique is applied to a scanning infrared surveillance system operating in poorly characterized but primarily low-frequency noise clutter environments. Near optimal performance is predicted both for continuous time and sampled data systems.© (1979) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Spatial Filtering Techniques For Infrared (IR) Sensors

A new least-mean-square filter is defined for signal-detection problems The technique is proposed for scanning IR surveillance systems operating in poorly characterized but primarily low-frequency clutter interference Near-optimal detection of point-source targets is predicted both for continuous-time and sampled-data systems

R. Nitzberg

Papers

Spatial Filtering Techniques For Infrared (IR) Sensors

Least-mean-square spatial filter for IR sensors.

Least-Mean-Square Spatial Filter for Infrared Sensors