The matched filter (MF) and adaptive coherence estimator (ACE) show great effectiveness in hyperspectral target detection applications. Practical applications in which on-board processing is generally required demand real-time or near-real-time implementation of these detectors. However, a vast amount of hyperspectral data may make real-time or near-real-time implementation of the detection algorithms almost impossible. Band selection can be one of the solutions to this problem by reducing the number of spectral bands. We propose a new band selection method that prioritizes spectral bands based on their influence on the detection performance of the MF and ACE and discards the least influential bands. We validate the performance of our method using real hyperspectral images. We also demonstrate our technique on near-real-time detection tasks and show it to be a feasible approach to the tasks.
Infrared search and track pursues the detection of sea-skimming infrared targets incoming from long distance. This paper presents a realistic synthetic target simulator for the development of infrared target detection and tracking algorithms. The proposed simulator consists of a 2-D background modeling part and a 3-D infrared target modeling part. Real infrared background images are used for the realistic modeling of background. Synthetic infrared target images are obtained by the consecutive processing of 3-D geometric modeling and radiometric modeling of targets according to target types, target distances, and atmospheric transmissivity. The experimental results validate the realistic modeling of the proposed method by comparing real observation sequence data.
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.
A mean shift algorithm has gained special attention in recent years due to its simplicity to enable real-time tracking.
However, the traditional mean shift tracking algorithm can fail to track target under occlusions. In this paper we propose
a novel technique which alleviates the limitation of mean shift tracking. Our algorithm employs the Kalman filter to
estimate the target dynamics information. Moreover, the proposed algorithm performs the background check process to
calculate the similarity which expresses how similar to target the background is. We then find the exact target position
combining the motion estimation by Kalman filter and the color based estimation by the mean shift algorithm based on
the similarity value. Therefore, the proposed algorithm can robustly track targets under several types of occlusion, while
the mean shift and mean shift-Kalman filter algorithms fail.
In the maritime environment, the main goals of an infrared search and track system is to search and track the targets
approaching to ships, such as sea skimming missiles, small ships, and aircrafts. In this paper, we propose a high
performance infrared search and track system. Our proposed infrared search and track system is composed of a dual band
infrared detection module, signal processing module, servo control module, and control console module. In the dual band
infrared detection module, the sensor head of our proposed system is organized by one-dimensional MWIR and LWIR
detectors (480X6) with 3-axes servo stabilization. The signal-processing module consists of several blocks such as a
target detection block, target tracking block, panoramic video displaying block, video input/output block, and system
control block. Those blocks perform the signal-processing algorithms involved with target search and tracking. In our
proposed system, adaptive temporal and spatial filtering methods, which can reduce background clutters effectively, are
used for target detection. Moreover, the extended Kalman filter and the integrated probabilistic data association (IPDA)
algorithm are adapted for target tracking. Therefore, our proposed infrared search and track system can increase the
defense ability of warships due to long range and high accuracy of target detection and tracking.
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.