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

Abstract

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.

Adjacent Infrared Multitarget Detection Using Robust Background Estimation

“…If the background level increases, the threshold increases automatically, which leads to constant false alarms. Recently, Kim and Lee proposed a hysteresis thresholdbased constant false alarm rate (H-CFAR) detector [23]. As shown in Figure 4(a), the original CFAR (O-CFAR) detector probes all the pixels above the noise level [24].…”

Thermal Infrared Small Ship Detection in Sea Clutter Based on Morphological Reconstruction and Multi-Feature Analysis

“…Most studies [11,23,25] indicated that the background is the comparatively dark sea surface and the ship targets might be relatively local brighter regions. According to these above-mentioned methods and theories, the accurate bright ship target detection algorithm for TIR image is established.…”

“…However, in real cases, dark ship targets whose infrared radiation is lower than surroundings also exist in the backlighting infrared images. To detect dark infrared ship targets, Equations (4), (9), (8), and (10) in the Steps 1-5 of Algorithm 1 can be replaced by Equations (5), (12), (11), and (13), respectively, to compute normalized IFSM of dark ship image. Meanwhile, Equations (5), (12), (11), and (14) in the Steps 6-10 of Algorithm 1 can be replaced by Equations (4), (9), (8), and (15), respectively, to compute normalized BCSM of dark ship image.…”

“…These methods try to extract the ship target from background via modeling on the premise of acquiring abundant knowledge about the infrared radiation characteristics of ship target and background, but it is difficult to meet these requirements. To solve this problem, Kim et al [11,12] estimated the background and distinguished the target from sea clutter by using the heterogeneous background removal filter. Furthermore, the statistical histogram curve transforms were also developed for the infrared maritime target detection based on the model assumption that the ship target region is much brighter than the background, such as the methods mentioned in [13,14].…”

Infrared LSS-Target Detection Via Adaptive TCAIE-LGM Smoothing and Pixel-Based Background Subtraction

“…Kim et al [9] analyzed the characteristics of regional cluster and removed the false detection by means of spatial attribute-based classifications, the heterogeneous background removal filter, and temporal consistency filter. Motivated by the background classification and coastal region detection, Kim et al [10] proposed a novel scene-dependent small target detection strategy involving the relationships between the geometric horizon and the image horizon. By classifying the infrared background types and detecting the littoral regions in omni-directional images, coastal regions can be detected by fusing the region map and curve map.…”