The application of space-borne lidar data in forestry is mainly focused on the inversion of forest parameters. However, there are many complex input parameters in the GORT(geometrical optical radiation transmission) model used in space-borne large-spot full-waveform lidar echo simulator, which are difficult to obtain and rely on empirical formulas. As a result, there is no unified and complete forest space model. In this paper, We perform data processing and feature parameter extraction on point cloud data, then we establish a forest echo model including sensor design parameters, vegetation and ground parameters in the target area, and laser radiation transmission characteristics. We compared the simulated forest echo wave with the corresponding GLAS wave in the experimental area. The results show that the correlation coefficient between the simulated forest echo waveform and the GLAS waveform is greater than 0.80.
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.
Validation of positioning accuracy is very important for assessment of high resolution satellite performance, which can be used as the reference of space optical remote sensor design. For stereo imagery, the general positioning accuracy assessment method can evaluate the positioning accuracy of target point but not the geometric size error of the observed scene. In combination with the circular error probability calculation, the positioning accuracy of a variety of points in the stereo images of WorldView-2 is contrasted and a geometric performance assessment method about geometric size of the observed scene is put forward, which shows that the difference between the two image positioning accuracy is about 47.2764 meters similarly and the geometric size error is only 3.309128 meters. This method is applicable to assessment the performance about geometric size when there is no large scale map or ground control point.
The striping noise can obscure the true radiation information in the images, reduce the accuracy of hyperspectral images, and have serious effect for visual interpretation and further results based on spectral analyses. This paper introduces the principle of moment matching method, emphasizes the analysis of the formative reason for “edge effect”, and then proposes an enhanced method to destripe EO-1/Hyperion data. The method for destriping has been introduced as well in this paper to compare with the moment matching method from both visual effect and quantitative assessment. It shows that the proposed method could achieve the greater effect for destriping EO-1/Hyperion data.
As a way of acquiring elevation with high accuracy, space-borne laser altimeter improves the capability of 3-dimensional cartography of satellite optical remote sensing imagery. However, the plane accuracy of space-borne laser altimeter is not so high as its elevation accuracy. Accordingly, the error souses and their influences on space-borne laser altimeter ground positioning are studied in this paper. The space-borne laser altimeter is very different from classical photogrammetry, the elevation information is obtained by measuring the time between sending and receiving the laser. As space-borne laser altimeter supplies laser echo signal other than image, the positioning accuracy is more important as well as the exterior orientation elements. The ground positioning of space-borne laser altimeter is first modeled, then error propagation of the model is studied, and the main error souses of space-borne laser altimeter ground positioning are obtained. At last the influences of each error souse on space-borne laser altimeter ground positioning are analysed as the references for space-borne laser altimeter designing and application.
An automatic SAR and optical images registration method based on improved SIFT is proposed in this paper, which is a
two-step strategy, from rough to accuracy. The geometry relation of images is first constructed by the geographic
information, and images are arranged based on the elevation datum plane to eliminate rotation and resolution differences.
Then SIFT features extracted by the dominant direction improved SIFT from two images are matched by SSIM as
similar measure according to structure information of the SIFT feature. As rotation difference is eliminated in images of
flat area after rough registration, the number of correct matches and correct matching rate can be increased by altering
the feature orientation assignment. And then, parallax and angle restrictions are introduced to improve the matching
performance by clustering analysis in the angle and parallax domains. Mapping the original matches to the parallax
feature space and rotation feature space in sequence, which are established by the custom defined parallax parameters
and rotation parameters respectively. Cluster analysis is applied in the parallax feature space and rotation feature space,
and the relationship between cluster parameters and matching result is analysed. Owing to the clustering feature, correct
matches are retained. Finally, the perspective transform parameters for the registration are obtained by RANSAC
algorithm with removing the false matches simultaneously. Experiments show that the algorithm proposed in this paper
is effective in the registration of SAR and optical images with large differences.
In this paper, we propose a registration method of high-resolution satellite images with geographic coordinates or
rational polynomial coefficients(RPC), which is the relationship between images and ground. Our approach consists of
two steps: firstly, a rough image registration is implemented on the basis of the "correction based on the projection"
theory that is an approximate epipolar line theory. Then, point features and line features in the image extracted by a
combination of corner extraction operators and line feature extraction algorithm will be the elements of the image
matching. A binding triangle net is constituted by all the features extracted to restrict the observed values. And in the end
of the process a high-precision automatic registration is performed by the least squares image matching method.
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