&lt;title&gt;New method for detection of dim point targets in infrared images&lt;/title&gt;

J Infrared Milli Terahz Waves

2010

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 LocalDirectional 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. Additionally, the adaptive Hysteresis threshold-based scheme is a suitable detection method. We validate the superior performance of the proposed method via three types of evaluation tests, including a real test scenario.

Section: Decision-based Target Detectionmentioning

confidence: 95%

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

J Infrared Milli Terahz Waves

2010

“…Similar results can be obtained by applying an iterative threshold [ 29 ]. Statistics-based adaptive threshold methods, such as the constant false alarm rate (CFAR), are useful in a severely cluttered background [ 30 , 31 ]. The simplest classification method is the nearest neighbor classifier (NNC) algorithm, which uses only feature similarity [ 32 ].…”

Section: Related Work In Terms Of Clutter Rejectionmentioning

confidence: 99%

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

Lee

2014

Sensors

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.

“…23 It is also possible to reduce false detection by decision method such as constant false alarm rate (CFAR), which is useful in a severely cluttered background. 24,25 If we apply the CFAR detector after a spatial filtering to an IRST image, we can have the detection results as shown in Fig. 1(b) where a lot of false detections caused by strong sun-glints, cloud clutter, and ground clutter for a given test image as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Target attribute-based false alarm rejection in small infrared target detection

2012

SPIE Proceedings

Infrared search and track is an important research area in military applications. Although there are a lot of works on small infrared target detection methods, we cannot apply them in real field due to high false alarm rate caused by clutters. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. Eight kinds of target features are extracted and analyzed statistically. Learning-based classifiers such as SVM and Adaboost are developed and compared with conventional classifiers for real infrared images. In addition, the generalization capability is also inspected for various infrared clutters.