This paper presents a high spatial resolution remote sensing image segmentation method by combining quadtree with minimum spanning tree. Firstly, the improved quadtree segmentation algorithm is used to divide the image iteratively into many over-segmented objects, which greatly facilitates the selection of initial segmentation parameters. Then the improved Morton coding is used to construct the spatial index of the generated over-segmented object and form the region adjacency relation. Combine spectral and texture features, the similarity between adjacent regions is calculated and the region merging criterion is constructed. Based on the idea of minimum spanning tree, the over-segmented objects are merged to generate multiple minimum spanning trees. During that process, the number of minimum spanning trees can be controlled to obtain ideal segmentation results. Compared with two other segmentation algorithms, the method proposed in this paper is more convenient to select segmentation parameters and has certain improvement in segmentation accuracy and object integrity of segmentation results.
Various high-resolution or high-frame-rate image sensors are used in star trackers to improve attitude accuracy and attitude update rate. However, the use of these high-performance image sensors has generated a new problem. The speed requirement of star detection and centroid calculation (SDCC) exceeds the capability of existing SDCC methods. Therefore, this paper presents a new real-time super-block-based SDCC method to resolve such a problem. In contrast to the traditional SDCC methods, the proposed method process the star images four by four and considering the continuous eight-connected pixels in the two lines as a super-block. The proposed method exhibits a speed that is four times faster than the previous SDCC methods. Thus, the performance limitation caused by the inadequacies of SDCC speed is solved. Experimental results indicate that the proposed method is correct and effective.
The images of a planet captured by the optical sensor system can provide a lot of serviceable navigation information for space exploration missions. Accurate measurement of a planet centroid is one of the important tasks for deep-space autonomous optical navigation. In order to improve the accuracy of centroid localization, a novel sub-pixel edge detection algorithm is proposed. First, image pre-processing technology is adopted to eliminate the effect of stars and stray light. Second, the edge of a planet at the pixel level is extracted using Sobel’s operator. Next, a new sub-pixel edge extraction algorithm of adaptive rotating template is proposed based on an improved partial area effect algorithm. Partial area effect algorithm is an effective edge detector based on an edge and acquisition model, which using the vertical or parallel template. We construct a new rotatable adaptive template, which is to align the direction of the template with the gradient direction of the edge pixel points. Finally, we estimated the planet center using least squares ellipse fitting. The result indicates that the algorithm can achieve higher location accuracy and robustness to noise.
In the remote sensing community, blur is a prevalent phenomenon especially for image using system parameter away from ideal truth. According to the relationship between dark channel and convolution, a modified and more applicable method is proposed here, which mainly contains blind kernel estimation and nonblind deconvolution. A reconstructed energy function, minimizing the sparsity and the value of dark channel, generates an accurate kernel; an effective module is introduced to preserve the texture and avoid artifacts; and finally a parallel framework is designed for large image. From the objective metrics on demo case, our approach is more effective to model and remove blurs than previous approaches, and furthermore we demonstrate its activity with experiments on real images.
Star tracker is an important instrument of measuring a spacecraft’s attitude; it measures a spacecraft’s attitude by matching the stars captured by a camera and those stored in a star database, the directions of which are known. Attitude accuracy of star tracker is mainly determined by star centroiding accuracy, which is guaranteed by complete star segmentation. Current algorithms of star segmentation cannot suppress different interferences in star images and cannot segment stars completely because of these interferences. To solve this problem, a new star target segmentation algorithm is proposed on the basis of mathematical morphology. The proposed algorithm utilizes the margin structuring element to detect small targets and the opening operation to suppress noises, and a modified top-hat transform is defined to extract stars. A combination of three different structuring elements is utilized to define a new star segmentation algorithm, and the influence of three different structural elements on the star segmentation results is analyzed. Experimental results show that the proposed algorithm can suppress different interferences and segment stars completely, thus providing high star centroiding accuracy.
Remote sensing images usually need scale-up for visualization or representation, using only one original image. According to the performance of detective sensors, a new and more applicable method is proposed here. To enhance the high-frequency components, the modulation transform function compensation (MTFC) part focuses on how to adjust the spatial response before and after launch, under signal-to-noise ratio control. This largely reduces the ring phenomenon from incorrect point spread function guesses. Then a contour stencil prior manages to limit edge artifacts in the upscaled image after MTFC. An iterative backprojection operation with fast convergence is also utilized to bring about intensity and contour consistency. We finally present our analysis based on real images with parallel design for full speed. Compared with existing algorithms, the operator illustrates its potential to keep geometric features and extend the visual and quantitative quality for further analysis.
Finding correct feature correspondence proves to be difficult in the process of image registration, especially for remote sensing images with background variation (e.g., images taken before and after an earthquake or flood) due to significant intensity differences in the same area. A robust and accurate point-matching method, called triangle transformation matching (TTM), is presented to increase the correct matching ratio and remove outliers. First, scale-invariant feature transform (SIFT) is used to extract the point features, and two preliminary point-matching sets can be obtained. Then, the spatial structure information around one point is compared to its corresponding point in the preliminary matching sets to verify whether they are inliers or not. This structure information is based on triangle area representation and it is affine invariant. A spatial consistency measure is used to remove outliers whose coordinates are very similar. Experiments compared with RANSAC, GTM, Bi-SOGC, and HTSC demonstrate the effectiveness of TTM under the conditions of background variation for remote sensing images.
The performance of star sensors largely depends on the accuracy of the model parameter estimation carried out through calibration. Measurement errors in the calibration data would cause estimated values to deviate from the actual values and couple with one another. Only the intrinsic parameters estimated through calibration are useful to star sensors. Thus, the coupling between estimated intrinsic and extrinsic parameters would affect star sensor accuracy. High-accuracy star sensors are significantly affected by such coupling between the aforementioned parameters. This study investigates the coupling between extrinsic and intrinsic parameters through calibration residual and starlight pointing error. A calibration method based on the decoupling of intrinsic and extrinsic parameters is then proposed. The proposed method uses the invariance of intrinsic parameters and estimates intrinsic parameters through simultaneous optimizations with distinct extrinsic parameters. In both the simulations and the experiment, the decoupling calibration effectively improves the accuracy of the intrinsic parameter estimation. Using the same calibration data, the intrinsic parameters’ estimation precision of a high-accuracy star sensor increased by a range from 50.83 to 86.47%.
Multitemporal remote sensing images generally suffer from background variations, which significantly disrupt traditional region feature and descriptor abstracts, especially between pre and postdisasters, making registration by local features unreliable. Because shapes hold relatively stable information, a rotation and scale invariant shape context based on multiscale edge features is proposed. A multiscale morphological operator is adapted to detect edges of shapes, and an equivalent difference of Gaussian scale space is built to detect local scale invariant feature points along the detected edges. Then, a rotation invariant shape context with improved distance discrimination serves as a feature descriptor. For a distance shape context, a self-adaptive threshold (SAT) distance division coordinate system is proposed, which improves the discriminative property of the feature descriptor in mid-long pixel distances from the central point while maintaining it in shorter ones. To achieve rotation invariance, the magnitude of Fourier transform in one-dimension is applied to calculate angle shape context. Finally, the residual error is evaluated after obtaining thin-plate spline transformation between reference and sensed images. Experimental results demonstrate the robustness, efficiency, and accuracy of this automatic algorithm.
A high speed CMOS image acquisition and transmission system, which is composed of CMOS image sensor IBIS5-A-1300, USB 2.0 interface chip EZ-USB FX2 and FPGA (Field Programmable Gate Array), is designed and developed.
The design of IBIS5-A-1300 driving timing, USB interface chip timing, firmware and application program are
introduced. Experiments show that the system possesses the advantage of high resolution and high frame rate, supports
single frame acquisition and video preview and fits the criterion of USB2.0 and the demand of real-time data
This paper introduces a fast-moving target tracking system based on CMOS (Complementary Metal-Oxygen
Semiconductor) image sensor. A pipeline parallel architecture of region segmentation and first order moment algorithms
on FPGA (Field Programmable Gate Array) platform enables driving the high frame rate CMOS image sensor and
processing real-time images at the same time, extracting coordinates of the bright target spots in the high-rate
consecutive image frames. In the end of this paper, an experiment proved that this system performs well in tracking fast-moving
target in satisfying demand of speed and accuracy.
Gait recognition is new biological identity technology and widely researched in recent years for its many advantages
compared with other biological identity technology. In this paper, we propose a simple but effective feature-compactness for gait recognition. First an improved background subtraction algorithm is used to obtain the silhouettes.
Then the compactness is extracted from the images in the gait sequence as the feature vector. In the step of classification,
DTW algorithm is adopted to adjust the feature vectors before classifying and two classifiers (NN and ENN) are used as
classifiers. Because of the simple features which we choose, it consumes little time for recognition and the results turn
out to be encouraging.
A designed star sensor must be extensively tested before launching. Testing star sensor requires complicated process
with much time and resources input. Even observing sky on the ground is a challenging and time-consuming job,
requiring complicated and expensive equipments, suitable time and location, and prone to be interfered by weather. And
moreover, not all stars distributed on the sky can be observed by this testing method. Semi-physical simulation in
laboratory reduces the testing cost and helps to debug, analyze and evaluate the star sensor system while developing the
model. The test system is composed of optical platform, star field simulator, star field simulator computer, star sensor
and the central data processing computer. The test system simulates the starlight with high accuracy and good
parallelism, and creates static or dynamic image in FOV (Field of View). The conditions of the test are close to
observing real sky. With this system, the test of a micro star tracker designed by Beijing University of Aeronautics and
Astronautics has been performed successfully. Some indices including full-sky autonomous star identification time,
attitude update frequency and attitude precision etc. meet design requirement of the star sensor. Error source of the
testing system is also analyzed. It is concluded that the testing system is cost-saving, efficient, and contributes to
optimizing the embed arithmetic, shortening the development cycle and improving engineering design processes.
A fast star tracking algorithm is proposed. In order to speed up the star tracking, three techniques including star catalogue partition, threshold mapping and sorting & matching are designed for three time-consuming portions in tracking algorithm,. Star catalogue partition divides the celestial sphere into small partitions. Threshold mapping sets a threshold of stars tracked which reduces mapping times. Sorting and matching avoided matching between stars which have a long distance. The software and hardware performance of tracking algorithm are stimulated. Tracking effectiveness and the success rate under different position noises are tested in software stimulation. In hardware stimulation, the tracking speed in every step is tested.
Adaptive decision feedback equalizer (ADFE) derived from transverse equalizer uses decision output signal to form a delay line, through which parts of coefficients are added together and are returned to the output. ADFE can compensate for communication channels with severe inter-symbol interference. The blur on image can be regarded as perturbation of the pixels, so in this paper a kind of ADFE is applied to 2D image restoration. There are two adaptive filers in this method, the forward filter has more flexibility to select its coefficients and is not direct inverse of blur transfer function. As a result, noise amplify will not happen. Additionally, the support of the object is determined with threshold. The experiments show that this new algorithm is robust and effectively when existing additive noise.
Star Sensor is an avionics instrument used to provide the absolute 3-axis attitude of a spacecraft utilizing star observations. It consists of an electronic camera and associated processing electronics. As outcome of advancing state-of-the-art, new generation star sensor features faster, lower cost, power dissipation and size than the first generation star sensor. This paper describes a star sensor anterior image acquisition and pre-processing hardware system based on CMOS image-sensor and FPGA technology. Practically, star images are produced by a simple simulator on PC, acquired by CMOS image sensor, pre-processed by FPGA, saved in SRAM, read out by EPP protocol and validated by an image process software on PC. The hardware part of system acquires images thought CMOS image-sensor controlled by FPGA, then processes image data by a circuit module of FPGA, and save images to SRAM for test. Basic image data for star recognition and attitude determination of spacecrafts are provided by it. As an important reference for developing star sensor prototype, the system validates the performance advantages of new generation star sensor.
The paper introduces a high speed image acquisition system for medical Electronic Endoscope based on PCI bus. The popular PCI controller-AMCC S5933 is utilized to implement PCI bus interface. FPGA is applied to control the data transferring and implement S5933 ADD_ON interface. The device driver of Windows is developed based on WinDriver. The experiment results show that the frequency of image acquisition can reach 33 MHz and the frame rate can be up to 50 fps at 800x600.
A full-sky autonomous star identification is presented. Star pattern is generated in the form of radial and cyclic features. Since radial pattern is a reliable feature, it is used for initial match, and cyclic pattern is then used in re-match step. Simulation is done to verify the performance and the result shows this method is more robust against location error compared with grid algorithm under the same condition.