Joohyoung Lee

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

This paper presents a novel mathematical method for incoming target detection in a cluttered background motivated by the robust properties of the human visual system (HVS). The robust detection of small targets is very important in IRST (Infrared Search and Track) applications for self-defense or attacks. HVS shows the best efficiency and robustness for the task of object detection in cluttered backgrounds. The robust properties of HVS include the contrast mechanism of figure-ground, multi-resolution representation of an object, size adaptation of object boundary, and pop-out phenomena in a complex environment. Based on these facts, a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and an optimization method. Simultaneous target signal enhancement and background clutter suppression are achieved by tuning and maximizing the signal-to-clutter ratio (TM-SCR) in Laplacian scale-space. At the first stage, Tune-Max of the signal to background contrast produces candidate targets with estimated target scale. At the second stage, Tune-Max of the signal-to-clutter ratio (SCR) produces maximal SCR that is used to sort the detection results. Especially, the row-directional-local background removal filter (RD-LBRF) is preprocessed in the horizontal region to enhance the TM-SCR method. The evaluation results of incoming target sequence validate the detection capability of the proposed method from dim, small targets to strong, large targets in comparison with the Top-hat method at the same rate of false alarms. The experimental results of various cluttered background images show that the proposed TM-SCR produces less false alarms (4.3 times reduction) compared to that of the Top-hat at the same detection rate. Finally, TM-SCR after RD-LBRF can maximize the detection rate in horizontal regions.

Small Infrared Target Detection by Region-Adaptive Clutter Rejection for Sea-Based Infrared Search and Track

This paper presents a region-adaptive clutter rejection method for small target detection in sea-based infrared search and track. In the real world, clutter normally generates many false detections that impede the deployment of such detection systems. Incoming targets (missiles, boats, etc.) can be located in the sky, horizon and sea regions, which have different types of clutters, such as clouds, a horizontal line and sea-glint. The characteristics of regional clutter were analyzed after the geometrical analysis-based region segmentation. The false detections caused by cloud clutter were removed by the spatial attribute-based classification. Those by the horizontal line were removed using the heterogeneous background removal filter. False alarms by sun-glint were rejected using the temporal consistency filter, which is the most difficult part. The experimental results of the various cluttered background sequences show that the proposed region adaptive clutter rejection method produces fewer false alarms than that of the mean subtraction filter (MSF) with an acceptable degradation detection rate.

Horizontal small target detection with cooperative background estimation and removal filters

Detecting small targets is essential for mitigating the sea-based Infrared search and track (IRST) problem. It is easy to detect small targets in homogeneous backgrounds such as the sky. When targets are on the border line of heterogeneous backgrounds such as the horizon in the sky and sea surface, solving the problem of detection becomes difficult. This paper presents a novel spatial filtering method, called Double Layered-Background Removal Filter (DL-BRF), for achieving high detection rates and low false alarm rates. DL-BRF consists of a Modified-Mean Subtraction Filter (M-MSF) and a consecutive Local-Directional Background Removal Filter (L-DBRF). M-MSF enhances the target signal and reduces background noise. L-DBRF removes horizontal structures, which upgrade the signal-to-clutter ratio and background suppression factor. L-DBRF used after M-MSF enhances the synergistic performance of horizontal target detection. We validate the superior performance of the proposed method via real evaluation tests.

Robust scale invariant target detection using the scale-space theory and optimization for IRST

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection in infrared search and track (IRST). The conventional spatial filtering methods with fixed sized kernel show limited target detection performance for incoming targets. The scale invariant target detection can be defined as searching for maxima in the 3D (x, y, and scale) representation of an image with the Laplacian function. The scale invariant feature can detect different sizes of targets robustly. Experimental results with real FLIR images show higher detection rate and lower false alarm rate than conventional methods. Furthermore, the proposed method shows very low false alarms in scan-based IR images than conventional filters.

Regularization approach to scene-based nonuniformity correction

Abstract. Various scene-based nonuniformity correction (SBNUC) methods have been proposed to diminish the residual nonuniformity (RNU) of the infrared focal plane array (IRFPA) sensors. Most existing SBNUC techniques require a relatively large number of image frames to reduce the RNU. In some applications, however, there is not enough time for capturing a large number of image frames prior to the camera operation, or only several image frames are available to users. A new scene-based approach that can correct the RNU using only several image frames is proposed. The proposed method formulates the SBNUC process as an energy minimization problem. In the proposed energy function, we introduce regularization terms for the parameter regarding the responsivity of the IRFPA as well as for the true scene irradiance. Correction results are obtained by minimizing the energy function using a numerical technique. Experimental results demonstrate the effectiveness of the proposed method.

Robust horizontal target detection with cooperative spatial filtering

A cooperative spatial filtering method is presented to detect small targets around horizontal region for infrared search and track (IRST). Double window filter (DWF) can enhance signal-to-noise ratio then directional background removal filter (DBRF) can subtract horizontal background structure. Experimental results present upgraded detection rate and false alarm rate.

Performances of multi-channel commander's sighting system for military land vehicle application

The developing multi-channel stabilized commanders sighting system which can be operated by day and night consists of 2nd generation LWIR thermal imager, daylight TV camera, eyesafe 1.54μm Raman shifted Nd:YAG laser rangefinder and direct view telescope for outstanding observation and fire control capabilities. The high performance thermal imager which uses a 480x6 HgCdTe array detector has dual field of views such as 3x2.25° in NFOV and 10x7.5° in WFOV. Daylight TV camera which employs 768x494 color CCD has 4.0cycles/mrad resolution and the same dual FOV. For an eyesafe operation, 1.54μm Raman shifted Nd:YAG laser rangefinder with InGaAs APD detector is incorporated into direct view optics to provide range data to commander and fire control computer with an accuracy of 10 meter. Multi channel EO/IR sensors for day and night views are integrated into the stabilized head mirror. In this paper, the performances of multi channel EO/IR sensors for the commanders sight has been analyzed for a tactical ground application.

Advanced thermal imaging system for tank sights

A new second generation advanced thermal imager, which can be used for battle tank sight has been developed in Korea. The IR optics has dual field of views such as 2.67×2° in NFOV and 10×7.5° in WFOV. This system uses a 480×6 TDI HgCdTe detector, operating in the 8-12μm wavelength, made by Sofradir. In order to correct non-uniformity of detector array, the two point correction method has been developed by using the thermo electric cooler. Additionally, to enhance the image of low contrast and improve the detection capability, the new technique of histogram processing has been proposed. Through these image processing techniques, we obtained the high quality thermal image.

A novel clustering method using weighted sub-sampling for an infrared search and track system

In an infrared search and tracking (IRST) system, the clustering procedure which merges target pixels into one cluster requires larger computational load according to increasing clutters. In this paper, we propose a novel clustering method based on weighted sub-sampling to reduce clustering time and obtain suitable cluster in cluttered environment. A conventional sub-sampling method can reasonably reduce clustering time but cause large error, when obtaining cluster center. However, our proposed clustering method perform sub-sampling and assign specific weights which is the number of target pixels in sampling region to sub-sampled pixels to obtain suitable cluster center. After performing clustering procedure, the cluster center position is properly obtained using sampled pixels and their weights in the cluster. Therefore, our proposed method can not only reduce clustering time using a sub-sampling method, but also obtain proper cluster center using our proposed weights. To validate our proposed method, experimental results for several infrared and noise images are presented.

Robust scale invariant small target detection using the Laplacian scale-space theory

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection in infrared search and track (IRST). The conventional spatial filtering methods with fixed sized kernel show limited target detection performance for incoming targets. The scale invariant target detection can be defined as searching for maxima in the 3D (x, y, and scale) representation of an image with the Laplacian function. The scale invariant feature can detect different sizes of targets robustly. Experimental results with real FLIR images show higher detection rate and lower false alarm rate than conventional methods. Furthermore, the proposed method shows very low false alarms in scan-based IR images than conventional filters.