A series of measurements of keystone and smile is required to assemble and aligning hyperspectral imagers. An efficient measurement method was proposed to calculate the keystone and the smile in a single measurement image by implementing an additional tool called the Field Identifier (FI) [1]. The measurement method is simple enough to make it possible to measure the wave front error (WFE) making minimum change from each measurement setup. To evaluate the accuracy of the measurement method, the positional data of 380 points were collected and 3σ was calculated along spectral and spatial axis. The measurement error calculated as 3σ is to be less than 1/10 of the performance goal to prove the effectiveness of the measurement method.
A three-mirror off-axis optical system was designed as a fore optics of a hyperspectral imager. The secondary mirror (M2) has an obscuration hole in the middle for the slit assembly. Despite of the disadvantages of having a slit hole in the mirror surface such as stray light defect and mirror surface fabrication difficulty, the configuration has great advantages of maximizing telecentricity while maintaining the wide field of view (FOV). [1] To evaluate the integrity of the optical system design, the stray light effect was analyzed including the spectrometer channel and confirmed that it has little effect on the image performance. Also, the RMS wave front error (WFE) of M2 is measured to be 20.12 nm exceeding our expectation including the edge of the hole. As a result, the optical system is aligned to have WFE less than 90 nm RMS in all fields. [1]
This paper presents a separate spatio-temporal filter based small infrared target detection method to address the sea-based
infrared search and track (IRST) problem in dense sun-glint environment. It is critical to detect small infrared targets such
as sea-skimming missiles or asymmetric small ships for national defense. On the sea surface, sun-glint clutters degrade
the detection performance. Furthermore, if we have to detect true targets using only three images with a low frame rate
camera, then the problem is more difficult. We propose a novel three plot correlation filter and statistics based clutter
reduction method to achieve robust small target detection rate in dense sun-glint environment. We validate the robust
detection performance of the proposed method via real infrared test sequences including synthetic targets.
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 the maritime environment, It is necessary for ship's self protection to search ad track approaching targets. We
developed high performance search and tracking system with Infrared sensors. Our system can obtain high performance
with several FPGAs and COTS processing boards. Dual band IR sensor (MWIR and LWIR) also gives two types of
target detection and tracing abilities. Our system designed to automatically detect and track both air and surface targets
such as sea skimming missiles, small ships, and aircrafts at a long range. In this paper, we describe technologies in our
search and tracking system architecture. We describe software architecture for signal processing and target detection and
tracking algorithms as well.
In this paper, we propose an overall target tracking scheme performing image stabilization, detection, tracking,
and classification in the IR sensored image. Firstly, in the image stabilization stage, a captured image is
stabilized from visible frame-to-frame jitters caused by camera shaking. After that, the background of the
image is modeled as Gaussian. Based on the results of the background modeling, the difference image between a
Gaussian background model and a current image is obtained, and regions with large differences are considered as
targets. The block matching method is adopted as a tracker, which uses the image captured from the detected
region as a template. During the tracking process, positions of the target are compensated by the Kalman filter.
If the block matching tracker fails to track targets as they hide themselves behind obstacles, a coast tracking
method is employed as a replacement. In the classification stage, key points are detected from the tracked image
by using the scale-invariant feature transform (SIFT) and key descriptors are matched to those of pre-registered
template images.
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.
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