Airborne laser bathymetric system has great advantages in shallow sea bathymetric mapping due to its no blind area, high accuracy and high density data. By using Monte Carlo method and radiation-transport equation, the spatial distribution of the signal spot on the sea surface is calculated respectively. The results show that the spatial distribution of the signal spot returned to the sea surface is more extensive with the increase of the depth, and the power attenuation of the center of the spot is more serious. In this paper, signal to noise ratio (SNR) is used as the performance evaluation criterion of laser bathymetry system, and the requirements of field of view for signal detection under different depth are analyzed. The analytic results will provide support for the design and optimization of the laser bathymetric system.
For the high-sensitivity cameras of a super-large star catalog, the conventional effective star identification methods for star sensors will face huge storage and calculations that current computers cannot afford. This paper presents a two-stage full-sky star identification method. 3~4 prominent stars are firstly quickly identified from a simplified star catalog, to determine the view direction. Then, three different strategies are adopted to recognize other remaining stars in the field of view: one strategy is to automatically load the K-vector table of the corresponding sky zone; one strategy is to temporarily generate a K-vector table from the candidate star set, and then remaining stars are identified according to the angular distance from the prominent stars; the third strategy is to obtain the image coordinates of the candidate star set, the proximity position constraint is considered while constraining the angular distance from the prominent stars. Experiments show that the speed of the third strategy is increased by about 20% and maintains a higher recognition rate (F1 is about 0.92). This two-stage recognition method ingeniously resolves the huge amount of calculation caused by the super-large star catalog, and can identify enough stars (ten thousand stars) in a single frame, and provides sufficient control points for the subsequent intrinsic calibration.
To overcome the difficulties in tracking flying targets in the sky under different conditions, a combined scheme is proposed to get a long-term tracklet: (1) point object is efficiently detected via a fast hypothesis test for background extraction, and an improved correlation filtering algorithm is utilized for possibly near object with much texture; (2) tracker is initialized and managed by estimating continuous motion using an acceleration Kalman filter; and (3) prior knowledge of object is further incorporated to remove false object in tracklet association process. Outdoor experiments prove that the proposed techniques improve the accuracy and reliability for target objects undergoing significant appearance variation due to cloud shift, abrupt motion, and temporary occlusion, and it also extends the validity of our strategy for further valuable applications.
Aerial sensors are widely used to acquire imagery for photogrammetric and remote sensing application. In general, the images have large overlapped region, which provide a lot of redundant geometry and radiation information for matching. This paper presents a POS supported dense matching procedure for automatic DSM generation from aerial imagery data. The method uses a coarse-to-fine hierarchical strategy with an effective combination of several image matching algorithms: image radiation pre-processing, image pyramid generation, feature point extraction and grid point generation, multi-image geometrically constraint cross-correlation (MIG3C), global relaxation optimization, multi-image geometrically constrained least squares matching (MIGCLSM), TIN generation and point cloud filtering. The image radiation pre-processing is used in order to reduce the effects of the inherent radiometric problems and optimize the images. The presented approach essentially consists of 3 components: feature point extraction and matching procedure, grid point matching procedure and relational matching procedure. The MIGCLSM method is used to achieve potentially sub-pixel accuracy matches and identify some inaccurate and possibly false matches. The feasibility of the method has been tested on different aerial scale images with different landcover types. The accuracy evaluation is based on the comparison between the automatic extracted DSMs derived from the precise exterior orientation parameters (EOPs) and the POS.
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.
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.
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.
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