Road Vector Map Change Monitoring Based on High Resolution Remote Sensing Images

Some studies about road vector map change detection were done in this paper. Firstly, on the basis of old road vector data, the original high resolution remote sensing image was cut into segments. Then, gray analysis and edge extraction of those segments were done so that changes of roads could be detected. Finally, according to the vector data and gray information of roads which were not changed, road templates were extracted and saved automatically. This method was performed on the World View high resolution image of certain parts in the country. The detection result shows that detection correctness is 79.56% and completeness can reach 97.72%. Moreover, the extracted road templates are essentials for the template matching method of road extraction.

Effects of local neighbourhood diversity on crown structure and productivity of individual trees in mature mixed-species forests

Background:Species-specific genotypic features,local neighbourhood interactions and resource supply strongly influence the tree stature and growth rate.In mixed-species forests,diversity-mediated biomass allocation has been suggested to be a fundamental mechanism underlying the positive biodiversity-productivity relationships.Empirical evidence,however,is rare about the impact of local neighbourhood diversity on tree characteristics analysed at a very high level of detail.To address this issue we analysed these effects on the individual-tree crown architecture and tree productivity in a mature mixed forest in northern Germany.Methods:Our analysis considers multiple target tree species across a local neighbourhood species richness gradient ranging from 1 to 4.We applied terrestrial laser scanning to quantify a large number of individual mature trees(N=920)at very high accuracy.We evaluated two different neighbour inclusion approaches by analysing both a fixed radius selection procedure and a selection based on overlapping crowns.Results and conclusions:We show that local neighbourhood species diversity significantly increases crown dimension and wood volume of target trees.Moreover,we found a size-dependency of diversity effects on tree productivity(basal area and wood volume increment)with positive effects for large-sized trees(diameter at breast height(DBH)>40 cm)and negative effects for small-sized(DBH<40 cm)trees.In our analysis,the neighbour inclusion approach has a significant impact on the outcome.For scientific studies and the validation of growth models we recommend a neighbour selection by overlapping crowns,because this seems to be the relevant scale at which local neighbourhood interactions occur.Because local neighbourhood diversity promotes individual-tree productivity in mature European mixed-species forests,we conclude that a small-scale species mixture should be considered in management plans.

Classification of CBERS-2 Imagery with Fuzzy ARTMAP Classifier

A fuzzy ARTMAP classifier is adopted for a classification experiment of CBERS-2 imagery. The fundamental theory and processing about the algorithm are first introduced, followed with a land-use classification experiment in Shihezi County on CBERS-2 high resolution imagery. Three classifiers are compared: maximum likelihood classifier (MLC), error back propagation (BP) classifier, and fuzzy ARTMAP classifier. The comparison shows comparably better results for the fuzzy ARTMAP classifier, with overall classification accuracy of 9.9% and 4.6% higher than that of MLC and BP. The results also prove that the fuzzy ARTMAP classifier has better discernment in identifying bare soil on CBERS-2 imagery.

Single Epoch GPS Deformation Signals Extraction and Gross Error Detection Technique Based on Wavelet Transform

Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and Mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.

EMD Based Multi-scale Model for High Resolution Image Fusion

高分辨率图象熔化是图象处理的域里的一个重要焦点。一个新图象熔化模型基于实验模式分解(EMD ) 的典型水平被介绍。多光谱的图象的紧张色彩浸透(代表耶稣之符号) 变换首先给紧张图象。此后，以 ID 的排列扩展， EMD 建模的 2D EMD 被用来从高分辨率的乐队图象和紧张图象分解详细规模图象和粗糙的规模图象。最后，一幅熔化紧张图象被重建在熔化图象与高分辨率的图象的高频率和紧张图象和代表耶稣之符号反的变换结果的低频率获得。在介绍 EMD 原则以后， 2D EMD 的一个多尺度的分解和重建算法被定义，一个熔化技术计划基于 EMD 是先进的。全色的乐队和多光谱的乐队 3,2,1 Quickbird 被用来估计熔化算法的质量。在在特定的排(列) 上根据 EMD 分析为兼并选择适当内在的模式函数(IMF ) 以后，象素灰色珍视系列，熔化计划给一幅熔化图象，它与通常使用的熔化算法相比(小浪，代表耶稣之符号， Brovey ) 。图象熔化的目的包括提高图象的可见性并且改进空间分辨率并且光谱原来的图象的信息。为了估计一幅图象的质量，在熔化，信息熵和标准差被使用估计熔化图象和相关系数的空间细节以后，为测量在原来的图象和熔化图象之间的失真以偏导索引和变弯的度光谱信息。为建议熔化算法，当 EMD 算法被用来执行熔化经验时，更好的结果被获得。

Finding the Asymptotically Optimal Baire Distance for Multi-Channel Data

A novel permutation-dependent Baire distance is introduced for multi-channel data. The optimal permutation is given by minimizing the sum of these pairwise distances. It is shown that for most practical cases the minimum is attained by a new gradient descent algorithm introduced in this article. It is of biquadratic time complexity: Both quadratic in number of channels and in size of data. The optimal permutation allows us to introduce a novel Baire-distance kernel Support Vector Machine (SVM). Applied to benchmark hyperspectral remote sensing data, this new SVM produces results which are comparable with the classical linear SVM, but with higher kernel target alignment.

Component-adaptive up-sampling for inter layer interpolation in scalable video coding

Scalable video coding （SVC） is a newly emerging standard to be finalized as an extension of H.264/AVC. The most attractive characters in SVC are the inter layer prediction techniques, such as Intra_BL mode. But in current SVC scheme, a uniform up-sampling filter （UUSF） is employed to magnify all components of an image, which will be very inefficient and result in a lot of redundant computational complexity. To overcome this, we propose an efficient component-adaptive up-sampling filter （CAUSF） for inter layer interpolation. In CAUSF, one character of human vision system is considered, and different up- sampling filters are assigned to different components. In particular, the six-tap FIR filter used in UUSF is kept and assigned for luminance component. But for chrominance components, a new four-tap FIR filter is used. Experimental results show that CAUSF maintains the performances of coded bit-rate and PSNR-Y without any noticeable loss, and provides significant reduction in computational complexity.