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.
For the satellite remote sensing data, it is necessary to evaluate the adjacency effect due to atmospheric scattering. Accurate modeling of the adjacency effect requires capabilities dealing with rugged areas and multiple scattering. In this paper, estimation of the adjacency effect is done by calculating the contribution of photons after the multiple scattering process through a many layered atmosphere. For the requirement of fast calculation in remote sensing simulation system, we adopt the approximate ISAACS 2-stream and flux adding method to model the adjacency effect. We evaluate the multiple scattering model by simulating the at-sensor radiance observed over synthetic rugged scenes under varying atmospheric conditions. Radiance comparisons with a single scattering model show good agreement in the clear atmosphere. Relative radiance differences are found to be about 11% in the dust atmosphere, increasing to 15% in the steep areas. Being coupled with the simulation model for remote sensing, it can be used in generation of simulated datasets and validation of the data processing algorithms.
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.
With the development of high resolution remote sensing satellite in recent years, the research of typical objects is connecting more and more closely with remote sensing applications. In the TDI CCD camera on-orbit imaging process, great changes will happen on solar angles at different time, causing a certain change of BRDF of most earth’s surface objects, and finally affect the remote sensing radiances, even imaging quality. In order to solve this problem, optimization of in-orbit parameters based on the ground objects BRDF is necessary. A detailed investigation about the global imaging area of ground objects characteristics is given in this paper. We inverse BRDF of different time based on Kernel-Driven BRDF model, establish database of ground objects BRDF, make a classification of ground objects characteristics, simulate imaging effect with radiative transfer model and degradation model of remote sensor, and then optimize imaging parameters according to the imaging quality requirement. The simulation results show that the contrast, definition and dynamic range of image have improved, the proposed method in this paper can set imaging parameters reasonably of different imaging conditions, improve the imaging quality of high resolution remote sensing satellites.
While executing tasks such as ocean pollution monitoring, maritime rescue, geographic mapping, and automatic
navigation utilizing remote sensing images, the coastline feature should be determined. Traditional methods are not
satisfactory to extract coastline in high-resolution panchromatic remote sensing image. Active contour model, also called snakes, have proven useful for interactive specification of image contours, so it is used as an effective coastlines extraction technique. Firstly, coastlines are detected by water segmentation and boundary tracking, which are considered initial contours to be optimized through active contour model. As better energy functions are developed, the power assist of snakes becomes effective. New internal energy has been done to reduce problems caused by convergence to local minima, and new external energy can greatly enlarge the capture region around features of interest. After normalization processing, energies are iterated using greedy algorithm to accelerate convergence rate. The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.
The research on harbor recognition from remote sensing image is a very complicated problem due to complex environment
and features. On this mentioned above, a method is presented to recognize the targets with various transformations on the
basis of harbor targets extraction. This paper investigates the environmental characteristics of the harbor target. After
preprocessing and segmenting real-time image, the inside region which reflects the nature of the harbor is extracted. From
inside region the moment invariants can be calculated that it has the invariability with displacement, rotation and scale.
According to the practical application, an experimental system based on harbor targets recognition is established. The
result indicates that the harbor targets in remote sensing images can be recognized accurately using the method presented.
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