Infrared thermal imagery is widely used in various kinds of aircraft because of its all-time application. Meanwhile, detecting ships from infrared images attract lots of research interests in recent years. In the case of downward-looking infrared imagery, in order to overcome the uncertainty of target imaging attitude due to the unknown position relationship between the aircraft and the target, we propose a new infrared ship detection method which integrates rotation invariant gradient direction histogram (Circle Histogram of Oriented Gradient, C-HOG) descriptors and the support vector machine (SVM) classifier. In details, the proposed method uses HOG descriptors to express the local feature of infrared images to adapt to changes in illumination and to overcome sea clutter effects. Different from traditional computation of HOG descriptor, we subdivide the image into annular spatial bins instead of rectangle sub-regions, and then Radial Gradient Transform (RGT) on the gradient is applied to achieve rotation invariant histogram information. Considering the engineering application of airborne and real-time requirements, we use SVM for training ship target and non-target background infrared sample images to discriminate real ships from false targets. Experimental results show that the proposed method has good performance in both the robustness and run-time for infrared ship target detection with different rotation angles.
In the all-time matching and navigation task, the aircraft applies real-time infrared images acquired by infrared imagery sensor to match the referenced visible image provided by the satellite for accurate location. However, the large difference between the infrared image and the visible image makes the task challenging. In this paper, for the sake of engineering application in the avionics system, we obtain real-time infrared images according to the flight trajectory, and then use them to match the referenced visible images. Furthermore, the HOG features are extracted respectively from real-time infrared images and referenced visible images to describe their feature similarity, for the purpose of accurate matching and localization. Experimental results demonstrate that our proposed method can not only realize the matching between airborne infrared and visible images, but also achieve high location accuracy, which shows good performance and robustness.
Ship detection from optical images taken by high-altitude aircrafts such as unmanned long-endurance airships and unmanned aerial vehicles has broad applications in marine fishery management, ship monitoring and vessel salvage. However, the major challenge is the limited capability of information processing on unmanned high-altitude platforms. Furthermore, in order to guarantee the wide detection range, unmanned aircrafts generally cruise at high altitudes, resulting in imagery with low-resolution targets and strong clutters suffered by heavy clouds. In this paper, we propose a low-resolution ship detection method to extract ships from these high-altitude optical images. Inspired by a recent research on visual saliency detection indicating that small salient signals could be well detected by a gradient enhancement operation combined with Gaussian smoothing, we propose the facet kernel filtering to rapidly suppress cluttered backgrounds and delineate candidate target regions from the sea surface. Then, the principal component analysis (PCA) is used to compute the orientation of the target axis, followed by a simplified histogram of oriented gradient (HOG) descriptor to characterize the ship shape property. Finally, support vector machine (SVM) is applied to discriminate real targets and false alarms. Experimental results show that the proposed method actually has high efficiency in low-resolution ship detection.
Star image blurred by aircraft vibration decreases location accuracy and probability of the star extraction. In this paper,
first, the influence of aircraft vibration on the star image captured by star sensors is analyzed, and the mathematical
model is deduced and established. Then, in order to overcome the adverse effects of star extraction and stabilize the
accuracy of star sensor in high dynamic environment, a restoration method for blurred star image using Richardson-Lucy
(RL) method is introduced. The experimental results indicate that the proposed method can effectively improve the star
image signal-to-noise ratio and the extraction accuracy.
A novel adaptive aircraft detection method based on level set processing and circle-frequency filter is proposed in this paper. First, the SBGFRLS (Selective Binary and Gaussian Filtering Regularized Level Set) method is used twice to find airport region of interest (ROI) and candidate aircraft areas by local segmentation and global segmentation, respectively, so that sizes of those possible target areas can be computed. Then, the circle-frequency (CF) filter method is utilized adaptively to detect target aircrafts in the airport ROI via the mean radius estimated by sizes of those candidate areas obtained before. Experimental results on real remote sensing airport images demonstrate the efficiency and accuracy of the proposed method.
Subject to limited resolution for targets in many satellite images, low-resolution airplane detection is still difficult and challenging, which plays an important role in remote sensing. In this paper, we propose a new method to detect lowresolution airplanes in satellite images. First, the image is preprocessed by combing the unsharp contrast enhancement (UCE) filtered image and the original image. Second, the Local Edge Distribution (LED), which is susceptible to objects owning clustered edges, e.g., airplane, is calculated to acquire the target candidate regions while restraining large background area. Then, a multi-scale fused gradient feature image is computed to characterize the shapes of targets instead of the original image to overcome the influence from the self-shadow and different coating colors of airplanes. After that, a designed airplane shape filter with a modulated item is used to detect and locate real targets, in which the modulated item can effectively measure the degree of coincidence between the patch region and the airplane shape. Finally, coordinates of target centers are computed in the filtered image. Experimental results demonstrate that the proposed algorithm is effective and robust for detecting low-resolution airplanes in satellite images under various complex backgrounds.
Local-sky star recognition algorithm is a process of recognizing the extracted stars in image by making use of the prior rough attitude of star sensor in celestial sphere. In order to improve the detection and response performance of star sensor working in dynamic condition, ICCD is applied to imaging stars. However, image taken by ICCD has more non-Gaussian noise and the energy of imaging star is unstable. So a local-sky star recognition algorithm using spatial triangular relationship as matching features is supposed to deal with the difficulties. In the first place, an index array is designed according to Guide Triangles, which is applied to construct Guide Triangle Index List. In the second place, a general directing range of star sensor boresight is calculated according to FOV of star sensor and the output of inertial guidance system, and then, the candidate Guide Triangles set in above region is obtained rapidly. In the third place, construct image triangle patterns by applying position and energy of the extracted stars in the image, and then match the image triangle patterns with the above candidate Guide Triangles set for two stages, until N(N≥2) groups of successfully matched triangles pairs with smallest matching deviations sum are obtained. At the last, the recognized Guide Stars have to be matched posterior referring to the principle of simulated sky image, and the recognition results of image stars are all obtained. The proposed algorithm has compact Guide Database structure, rapid local-sky guide triangles obtaining, and good recognition correction percentage, even it has worse star location precision and more false stars. The simulation tests are performed to validate the relative efficiency and adaptation of the algorithm.
In this paper, we present a novel automatic image segmentation method, which combines the active contour method and
the saliency map method. The saliency map which is obtained by inversing the spectral residual of the image brings a
priori knowledge to bear on the image segmentation. The initial level set function is constructed from saliency map. In
this way, an automatic initialization of the level set evolution can be obtained. This method can minimize the iterations
of the level set evolution. The efficiency and accuracy of the method are demonstrated by the experiments on the
synthetic and real images.