Oil tank is one kind of foundational industrial facility for storage of oil and petrochemical products. Automatic recognition of the oil depot in the remote sensing image is of important practical significance in many fields. Nowadays, the Unmanned Aerial Vehicle (UAV) provides an available alternative solution to the satellite for monitoring the oil depot, owing to its advantages of flexibility, rapid response and minimal cost. In this paper, a novel oil tank extraction method based on detection of the elliptic rooftop is proposed. To start with, straight line segments of object boundary are extracted in the UAV imagery. Secondly, these lines are linked to form arc segments based on proper geometric criteria, and then elliptical rooftops are extracted based on these arcs to generate hypotheses of potential oil tanks. Finally, within Region of Interest (ROI) of rooftops, hypotheses disambiguation and verification of targets are accomplished primarily by extraction of facade contours of oil tanks. Experimental results demonstrate the good performance of our method on a variety of complex scenes.
Due to the broader extent of observation and higher detection probability of space targets, large FOV (field of vision) optical instruments are widely used in astronomical applications.. However, the high density of observed stars and the distortion of the optical system often bring about inaccuracy in star locations. So in large FOV observations, many conventional star identification algorithms do not show very good performance. In this paper, we propose a star identification method with a low requirement for observation accuracy and thus suitable for large FOV circumstances. The proposed method includes two stages. The former is based on the match group algorithm, in addition to which we exploit the information of differential angles of inclination for verification. The inclinations of satellite stars are computed by reference to the selected pole stars. Then we obtain a set of identified stars for further recognition. The latter stage involves four steps. First, we derive the relationship between the rectangular coordinates of catalog stars and sensor stars with the identified locations obtained. Second, we transform the sensor coordinates to the catalog coordinates and find the catalog stars at close range as candidates. Third, we calculate the angle of inclination of each unidentified sensor star in relation to the nearest previously identified one, and the angular separation between them as well, to compare with those of the candidates. At last, candidates satisfying the limitations are considered the appropriate correspondences. The experimental results show that in large FOV observations, the proposed method presents better performance in comparison with several typical star identification methods in open literature.
Image smear, produced by the shutter-less operation of full-frame charge-coupled device (CCD) sensors, greatly affects the performance of target detection, the centering accuracy, and visual magnitude estimation. We study the operation principle of full-frame CCDs, analyze the cause and properties of smear effect, and propose a smear removal algorithm for star images of full-frame CCDs. The proposed method locates the smears and extracts the rough profiles of the smeared stars by finding the conditional extrema. Then Gaussian fitting is applied to accurately extract the stars, in order to maintain the integrity of star images while minimizing the smear effect. The extraction of smears and stars requires parameters such as the size of the CCD, the integration time and the readout time, as well as the estimation of background noise. We assess the performance of our scheme with real observed data. The experimental results show that the proposed scheme improves the average signal-to-noise ratio of the images by about 22%, presenting better smear removal performance compared with several published methods. The limitation of the proposed algorithm includes the difficulty of distinguishing between two very close stars displaying the gray level of a single peak and overestimation of the background noise may also influence the performance of the algorithm.
Aircraft recognition is of great theoretical and practical significance in fields like remote sensing, navigation and traffic monitoring. It seems difficult to recognize aircraft in low-resolution SAR imagery because of difference between real image and simulated template induced by poor image quality and inherent simulation error. Aiming at this problem, an aircraft recognition method using peak feature matching is proposed. Firstly, the scattering centers of detected target are extracted in low-resolution SAR imagery using an adaptive threshold. Secondly, the extracted peak features are used to estimate the aircraft azimuth angle, which can be used to reduce the searching space in template database dramatically. Finally, a novel peak feature matching method using spatial distribution information of entire peak set is proposed to measure the similarity between detected target and simulated template. Experimental results demonstrate the good performance of the proposed method on a variety of low-resolution SAR imageries.
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.
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.
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.
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.
An approach to infrared ship detection based on sea-sky-line(SSL) detection, ROI extraction and feature recognition is proposed in this paper. Firstly, considering that far ships are expected to be adjacent to the SSL, SSL is detected to find potential target areas. Radon transform is performed on gradient image to choose candidate SSLs, and detection result is given by fuzzy synthetic evaluation values. Secondly, in view of recognizable condition that there should be enough differences between target and background in infrared image, two gradient masks have been created and improved as practical guidelines in eliminating false alarm. Thirdly, extract ROI near the SSL by using multi-grade segmentation and fusion method after image sharpening, and unsuitable candidates are screened out according to the gradient masks and ROI shape. Finally, we segment the rest of ROIs by two-stage modified OTSU, and calculate target confidence as a standard measuring the facticity of target. Compared with other ship detection methods, proposed method is suitable for bipolar targets, which offers a good practicability and accuracy, and achieves a satisfying detection speed. Detection experiments with 200 thousand frames show that the proposed method is widely applicable, powerful in resistance to interferences and noises with a detection rate of above 95%, which satisfies the engineering needs commendably.