海洋遥感图像亚像素配准算法关键技术研究
[Abstract]:The offshore marine environment, marine disasters, and marine emergencies usually have the characteristics of fast dynamic changes (the fast change of the hour level), and the daily observed solar synchronous orbit (polar orbit) ocean satellite is difficult to meet the needs of diurnal change monitoring. The stationary orbit satellite can use the same remote sensor to connect the same area of interest to the same area. Continuous observation is the best means to carry out high frequency to earth observation. However, any satellite platform is disturbed by flutter. Because the integration time of the satellite imaging system of still orbit is generally long, these flutter will seriously affect the quality of remote sensing image. At present, the technique of measuring and inhibiting the flutter measurement and suppression of the satellite platform has developed to a higher level. The satellite platform suppression technology and related equipment can eliminate most of the platform flutter, but the attitude drift of the low frequency satellite platform is helpless. The orbit height of the stationary orbit ocean imaging radiometer is 35800km, its detection band is 8 wavelengths of visible to near infrared, the angular resolution is 7? Rad, and the ground resolution (below the star point) 250m, The 2048 x 2048 element silicon CMOS array detector LUPA4000. is highly sensitive to the attitude drift of the satellite platform in the imaging process because of the high orbit height of the stationary satellite ocean imaging radiometer, the weak reflection energy of the ocean and the high ground resolution, so that the signal to noise ratio of the system can be increased by multiple accumulating methods. The visible light module of the radiometer is 16 accumulation. In addition, the satellite platform has low frequency attitude drift. According to the data of the satellite platform, the stability of the satellite platform is 5 * 10-4? So the image is most offset by 1.8 pixels during the 16 accumulating process. If it is not processed directly, it must be added directly. In this paper, the sub pixel registration algorithm of remote sensing image is first proposed in this paper. Firstly, the image is divided into different sub images according to the optimal criterion. Secondly, the image is extracted by using the Canny algorithm based on Ostu, and then the extracted image is calculated by SURF. The feature points are extracted by the method, and the window size is 200 x 200 pixels around the key point. The sub pixel offset is calculated by matrix multiplication phase correlation method in the window. The sub pixel offset of the whole image is finally obtained by using the offset of all the sub images. On the basis of simulation, on the basis of simulation, in order to improve the processing speed of the future algorithm hardware, this paper proposes an improved Shannon entropy low information feature point elimination algorithm and an improved SURF algorithm: improved Shannon entropy low information quantity feature point elimination algorithm reduces the number of parameters and matches, and improves the speed of execution of the algorithm. The improved SURF algorithm reduces the dimension of the feature vector descriptor from the original 64 dimension to 36 dimension, which can obviously improve the matching speed of the feature points, and the improved SURF algorithm can multichannel parallel processing. These improvements will greatly enhance the hardware execution speed of the algorithm. The research content and innovation point of this paper are the following 4 aspects: 1) The size of remote sensing image is 2048 x 2048, so the storage and computing resources will be very huge in the process of processing. The general hardware such as FPGA and DSP will not be processed. Therefore, the sub pixel registration algorithm of remote sensing image is processed in block and parallel processing, and the speed of implementation of the algorithm is also solved as well as FPGA, DSP and so on. The problem of ultra large size remote sensing image can not be handled by hardware. And because of the phase correlation method based on matrix multiplication, the subpixel registration algorithm of remote sensing image has obvious suppression effect on noise. Therefore, even if the remote sensing image has noise, this algorithm can still obtain high precision estimation of sub pixel offset. 2) the improved Shannon entropy low information feature point elimination: this algorithm not only greatly reduces the number of feature points of the participation matching, but also improves the correct rate of matching. Therefore, it can obviously improve the arithmetic speed.SURF algorithm to obtain many feature points, however, there are many unpaired points in the image matching process. In this case, this paper proposes an improved Shannon entropy feature point elimination algorithm with low information quantity. The improvement is mainly reflected not only in the discrete pixel values of the characteristic regions but also on the relationship between the feature center point and the other pixels in the surrounding area.3). The improved SURF algorithm realizes the parallelism of the main direction calculation and the generation of feature vector descriptors. At the same time, the eigenvector descriptors are reduced from the original 64 dimension to 36 dimension. These improvements not only improve the execution speed of the algorithm, but also improve the accuracy of the matching. The improvement of the SURF algorithm is mainly reflected in the use of the gradient of the feature points in the radial gradient in the row substitution, which can implement the feature descriptor. Rotation invariance. In the generation of feature vector descriptors, the traditional SURF algorithm uses a square area, the size of the region is 20S x 20S (S is the scale of the scale space in which the feature is located). Instead, the improved SURF algorithm uses a circular region with a radius of 20S, omitting the rotation step of the coordinate system. The circular region of the 20S is taken. It is divided into 9 characteristic subregions, each subregion is described with 4 features, thus a 36 dimension eigenvector descriptor is generated, and the dimension.4 of the descriptor is greatly reduced. Based on the detailed study and experimental verification of the sub pixel registration algorithm for remote sensing images, the improved feature point extraction algorithm of the SURF algorithm is introduced in detail. The hardware architecture, the hardware architecture of the matrix multiplication phase correlation method subpixel offset estimation and the hardware architecture based on the regression learning image interpolation amplification algorithm. Based on the in-depth study of the principle and steps of the algorithm, the algorithm is adapted to the subdivision of hardware and hardware implementation, and the sub pixel registration algorithm of the later remote sensing image is given. Hardware implementation provides the basis and guidance.
【学位授予单位】:中国科学院大学(中国科学院上海技术物理研究所)
【学位级别】:博士
【学位授予年份】:2017
【分类号】:TP751
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