With regard to aircraft target detection in complex clouds background of infrared search system, this paper proposes a new target detection algorithm based on combination of intensity and edge of the target. Firstly, the algorithm segments the image by iterative OTSU segmentation method, at the same time, it detects the edge by morphological processing. Then, by the fusion decision of the combination of segmentation and edge result, it detects the real aircraft targets and eliminates the clouds false alarm. The algorithm overcomes the too much clouds false alarm problems of the traditional target detection method. The test data detection shows, the algorithm enables effective detection of aircraft target in complex clouds background with low-rate false warning. The algorithm has realized real-time processing and has been effectively applied to the development of the engineering sample of the Wide Field of View Infrared Search System.
The typical probability based point pattern matching method is coherent point drift (CPD) algorithm, which treats one point set as centroids of a Gaussian mixture model, and then fits it to the other. It uses the expectation maximization framework, where the point correspondences and transformation parameters are updated alternately. However, the anti-outlier performance of CPD is not robust enough as outliers have always been involved in the operation until the CPD converges. Hence, an automatic outlier suppression (AOS) mechanism is proposed. First, outliers are judged by a matching probability matrix. Then, transformation parameters are fitted using accurate matching point sets. Finally, the Gaussian centroids are forced to move coherently by this transformation model. AOS-CPD can efficiently improve the anti-outlier performance of rigid CPD. Furthermore, CPD is applied to image matching. A new local changing information descriptor-relative phase histogram (RPH) is designed and RPH-AOS-CPD is proposed to embed RPH measurement into AOS-CPD as a constraint condition. RPH-AOS-CPD makes full use of grayscale information besides having an excellent anti-outlier performance. The experimental results based on both synthetic and real data indicate that compared with other algorithms, AOS-CPD is more robust to outliers and RPH-AOS-CPD offers a good practicability and accuracy in image matching applications.
In order to enhance the robustness of building recognition in forward-looking infrared (FLIR) images, an effective
method based on big template is proposed. Big template is a set of small templates which contains a great amount of
information of surface features. Its information content cannot be matched by any small template and it has advantages
in conquering noise interference or incompleteness and avoiding erroneous judgments. Firstly, digital surface model
(DSM) was utilized to make big template, distance transformation was operated on the big template, and region of
interest (ROI) was extracted by the way of template matching between the big template and contour of real-time image.
Secondly, corners were detected from the big template, response function was defined by utilizing gradients and phases
of corners and their neighborhoods, a kind of similarity measure was designed based on the response function and
overlap ratio, then the template and real-time image were matched accurately. Finally, a large number of image data was
used to test the performance of the algorithm, and optimal parameters selection criterion was designed. Test results
indicate that the target matching ratio of the algorithm can reach 95%, it has effectively solved the problem of building
recognition under the conditions of noise disturbance, incompleteness or the target is not in view.
In this paper, we propose an unsupervised change detection method using the labeled co-occurrence matrix on multitemporal
SAR images. In SAR images, each land cover (LC) class has a distinct reflectivity to radar signals and presents
a specific backscattering value. Generally, the amplitude of the SAR images can be roughly clustered into three classes
according to the backscattering behaviors of the LC classes. The changes occurred between the images can be considered
as a backscattering variation that is changed from one backscattering class into another. As a result, we analyzed the
possible cases of the positive and negative backscattering variations, and merged the initial three backscattering classes
into two classes with the pixel in the medium backscattering class being attached to the strong backscattering class and
the low backscattering class respectively in a membership degree. Two pairs of fuzzy-label images are derived
accordingly, where each pair of fuzzy-label images are computed from the multi-temporal SAR data. The labeled cooccurrence
matrix is computed locally on each pair of fuzzy-label images by combining the membership values in a
conjunctive operator, and the autocorrelation feature is extracted. The classifications are implemented by Otsu Nthresholding
algorithm on the derived two autocorrelation features. The final binary change detection map is achieved by
combining the obtained two classification results. Experiments were carried on portions of multi-temporal Radarsat-1
SAR data. The effectiveness of the proposed approach was confirmed.
Point pattern matching (PPM) including the hard assignment and soft assignment approaches has attracted much attention.
The typical probability based method is Coherent Point Drift (CPD) algorithm, which treats one point set(named model
point set) as centroids of Gaussian mixture model, and then fits it to the other(named target point set). It uses the
expectation maximization (EM) framework, where the point correspondences and transformation parameters are updated
alternately. But the anti-outlier performance of CPD is not robust enough as outliers have always been involved in
operation until CPD converges. So we proposed an automatic outlier suppression mechanism (AOS) to overcome the
shortages of CPD. Firstly, inliers or outliers are judged by converting matching probability matrix into doubly stochastic
matrix. Then, transformation parameters are fitted using accurate matching point sets. Finally, the model point set is forced
to move coherently to target point set by this transformation model. The transformed model point set is imported into EM
iteration again and the cycle repeats itself. The iteration finishes when matching probability matrix converges or the
cardinality of accurate matching point set reaches maximum. Besides, the covariance should be updated by the newest
position error before re-entering EM algorithm. The experimental results based on both synthetic and real data indicate that
compared with other algorithms, AOS-CPD is more robust and efficient. It offers a good practicability and accuracy in
rigid PPM applications.
High-resolution, multi-pixels and large field of view (FOV) infrared (IR) detector is an important research direction, which greatly improves the target detection capability. This paper addresses the infrared target detection under the
guidance of attention mechanism. The Gabor filter is used to extract the elementary visual feature of infrared image for its
orientation selectiveness. Then it researches the reasons that produce visual saliency in frequency domain, and provides the
multichannel feature combination strategy to generate the feature map. Further, a novel saliency detection model using Fourier spectrum filtering, is presented to calculate feature regions of infrared image. Experimental results using a wide range of real IR images demonstrate that the proposed algorithm is robust and effective, yielding satisfying results for infrared target detection in large FOV with complex background and low SNR.
In order to implement real-time detection of hedgehopping target in large view-field infrared (LVIR) image, the
paper proposes a fast algorithm flow to extract the target region of interest (ROI). The ground building region was
rejected quickly and target ROI was segmented roughly through the background classification. Then the background
image containing target ROI was matched with previous frame based on a mean removal normalized product correlation
(MRNPC) similarity measure function. Finally, the target motion area was extracted by inter-frame difference in time
domain. According to the proposed algorithm flow, this paper designs the high-speed real-time signal processing
hardware platform based on FPGA + DSP, and also presents a new parallel processing strategy that called function-level
and task-level, which could parallel process LVIR image by multi-core and multi-task. Experimental results show that
the algorithm can extract low altitude aero target with complex background in large view effectively, and the new design
hardware platform could implement real time processing of the IR image with 50000x288 pixels per second in large
view-field infrared search system (LVIRSS).
Airport runway recognition is of great significance in fields like remote sensing, navigation and traffic monitoring. An airport runway recognition method using the “hypothesize-and-verify” paradigm is proposed. Firstly, local line segments of runway contour are extracted in complex infrared image. Secondly, basing on a new Line Segment Hough Transform, local line segments vote fuzzily in the parameter space to obtain global line segment clustering, and then parallel straight lines are extracted on the basis of parameter space to form hypotheses of potential airport runways. Finally, using contextual information of airport constructions, hypotheses disambiguation and verification of runway is accomplished primarily by extraction of runway markings and segmentation of transportation network, i.e. taxiways and apron. Experimental results demonstrate the good performance of our method on a variety of complex scenes.
Conventional methods often assume that water region is homogeneous and bridge is brighter than background. They usually recognize target by parallel lines detection. But grayscale of bridge has bipolar problem in FLIR images due to interference of complex background and constraints of imaging conditions, which means that it can be greater or lower than river. Furthermore, water is not a homogeneous area as a whole because of the interference of water clutter and shoals. This paper proposes a novel algorithm of bridge recognition based on Gabor filter. Firstly, we obtain target ROI by extracting the horizontal line. And then ROI sub-images are enhanced by Gabor filter and target polarity is determined by bridge body detection. Finally, bridge recognition can be achieved by pier detection according to the target polarity and location of bridge body. Experimental results of nearly 3000 frames show that the proposed algorithm can effectively overcome problems such as bipolar target and low image contrast. It offers a good practicability and accuracy in bridge recognition in FLIR images.
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