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151.
本文通过对三种矢量数据动态存贮方法的分析比较,并通过实践,找到一种安全可靠并且比较实用的数据动态存贮方式。 相似文献
152.
基于交互多模型的水下目标跟踪方法 总被引:1,自引:0,他引:1
According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference (FIMM) is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model (CA), the Singer mode~, and the nearly constant speed horizontal-turn model (HT) in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm. 相似文献
153.
An iterative algorithm to calculate mutual correlation using hierarchical key points and the search space mark principle is proposed. An effective algorithm is designed to improve the matching speed. By hierarchical key point algorithm and mutual correlation coefficients of the matching images, the important points can be iteratively calculated in the images hierarchically, and the correlation coefficient can be obtained with satisfactory precision. Massive spots in the parameter space which are impossible to match can be removed by the search space mark principle. Two approximate continuities in the correlation image matching process, the image gray level distribution continuity and the correlation coefficient value in the parameter space continuity, are considered in the method. The experiments show that the new algorithm can greatly enhance matching speed and achieve accurate matching results. 相似文献
154.
155.
Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 1-km vegetation index products, daily temperature, photosynthetically active radiation (PAR), and precipitation from 2001 to 2004 were utilized to analyze the temporal variations of the MODIS normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), as well as their correlations with climate over the evergreen forested sites in Zhejiang-a humid subtropical region in the southeast of China. The results showed that both NDVI and EVI could discern the seasonal variation of the evergreen forests. Attributed to the sufficient precipitation in the study area, the growth of vegetation is mainly controlled by energy; as a result, NDVI, and especially EVI, is more correlated with temperature and PAR than precipitation. Compared with NDVI, EVI is more sensitive to climate condition and is a better indicator to study vegetation variations in the study region 相似文献
156.
An Efficient Reordering Prediction-Based Lossless Compression Algorithm for Hyperspectral Images 总被引:8,自引:0,他引:8
In this letter, we propose an efficient lossless compression algorithm for hyperspectral images; it is based on an adaptive spectral band reordering algorithm and an adaptive backward previous closest neighbor (PCN) prediction with error feedback. The adaptive spectral band reordering algorithm has some strong points. It can adaptively determine the range of spectral bands needed to be reordered, and it can efficiently find the optimum branches. Hyperspectral images have a large number of spectral bands, which express the same land cover structure and have high correlation. The adaptive backward PCN prediction with error feedback can sufficiently make use of this correlation. Experiments show that implementing both the reordering of the spectral bands before prediction and the prediction with error feedback improve compression performance 相似文献
157.
158.
Wenzhong Shi Kimfung Liu Hua Zhang 《International Journal of Applied Earth Observation and Geoinformation》2011
The multiple classifier system (MCS) is an effective automatic classification method, useful in connection with remote sensing analysis techniques. Combining MSC with induced fuzzy topology enables a decomposition of image classes. This fuzzy topological MCS then provides a new and improved approach to classification. The basic classification methods discussed in this paper include maximum likelihood classification (MLC), minimum distance classification (MIND) and Mahalanobis distance classification (MAH). 相似文献
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160.