Clouds’ macrophysical characteristics play an important role in the climate system and dramatically vary because of the diverse climatic and geographic factors in China. We analyze cloud macrophysical characteristics and the differences between subregions in China (18°–54°N, 73°–135°E) from March 2012 to February 2015 based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, including cloud fractions, cloud vertical distribution, and cloud geometrical properties with the perspective of daytime and nighttime. We found that annual single layer, multilayer (ML), and total cloud fractions are 40.4±1.1%, 22.4±0.4%, and 62.8±1.5%, respectively, and clouds are generally located between 6 and 12 km. The cloud fractions in daytime are less than that in nighttime over the south while that of Tibet shows the reverse trend. In the vertical direction, except for Tibet, the clouds in nighttime have larger spatial coverage and are higher in altitude than that in daytime. The regional average values of cloud macrophysical characteristics in the south are highest, followed successively by Tibet, north, and northwest. Cloud geometrical depth and spacing show a gradually declining trend with the increase in layers and decrease of altitude in ML cloud system.
To solve the problem that the H/α-Wishart unsupervised classification algorithm can generate only inflexible clusters due to arbitrarily fixed zone boundaries in the clustering processing, a refined fuzzy logic based classification scheme called the H/α-Wishart fuzzy clustering algorithm is proposed in this paper. A fuzzy membership function was developed for the degree of pixels belonging to each class instead of an arbitrary boundary. To devise a unified fuzzy function, a normalized Wishart distance is proposed during the clustering step in the new algorithm. Then the degree of membership is computed to implement fuzzy clustering. After an iterative procedure, the algorithm yields a classification result. The new classification scheme is applied to two L-band polarimetric synthetic aperture radar (PolSAR) images and an X-band high-resolution PolSAR image of a field in LingShui, Hainan Province, China. Experimental results show that the classification precision of the refined algorithm is greater than that of the H/α-Wishart algorithm and that the refined algorithm performs well in differentiating shadows and water areas.
A new method for circle detection, Hough gradient clustering method, has been developed in this paper. By using
gradient direction angle to find the diameter of a circle, the new method can rapidly detect the circle in a complex
background. The crucial steps in this method are the feature extraction and the clustering of the points which have the
same gray direction angle and are collinear along the gradient direction. The application of the two-to-one space
mapping and 1-2Hough transform can further reduce the useless calculation in the process of circle detection. Comparing
with the Hough gradient method in OpenCV, the newly developed method shows a higher efficiency of circle detection
in a complex background image as well as a great improvement in the anti-noise ability.
The authors introduce unsupervised Wishart classification technique for fully polarimetric SAR data based on H-alpha
decomposition of POLSAR images, and applied this technique to AIRSAR data of Flevoland, Netherlands. By applying
the Cloude H-alpha decomposition to the original L-band image, we segment the image to 9 classes. While take this as
the initial input, Wishart classification is followed. The most valuable in this paper is the section of application analysis.
We found H-alpha classification has lower classification accuracy than Wishart iteration which use coherence matrix, but
why? By analyzing the classification results for each type of land cover, this paper concluded the reason is that
parameters of entry and alpha angle lose the original polarimetric information. While coherence matrix does not lose the
original polarimetric information, we suggest that directly use coherence matrixes could derive much higher
classification accuracy. There is also another found. Middle entropy scattering such as low vegetation often does not a
single target while high or low entropy scattering, such as the deep forest and water, the coverage relatively much denser,
often has single component; thus, the classification accuracy of high of low entropy land cover will be much higher than
middle entropy scattering.
SAR is a side-looking imaging mode and its image is very sensitive to the terrain shape. The little undulation of the terrain may induce the change of the image gray distribution and/or the texture characteristics. In this paper, the radarclinometry for extracting ditital elevation from single SAR image is investigated, which is based on the shape-from-shading principle developed in computer vision, consists in estimating the geometric parameters of a ground, from its radiometry and more precisely from the backscattered intensity coming from a piece of imaged ground. Firstly, the approaches for generation of the digital elevation from the SAR image data are discussed. Secondly the method of radarclinometry will be briefly described. The elevation reconstruction relies on the Lambertiam assumption for the terrain backscatter model. Then a single-line integral process is applied to calculate each pixel altitude, but it is still contaminated by noise. Finally the multi-line integral processing with various directions and the simulated annealing algorithm are respectibvely introduced to improve the single-line integral processing result. The presented experiment results promising in many geographic applications. This is an interesting technique of relief restoring, because it uses one single image only.
The elliptic curve cryptographic random sequences as watermark are embedded in wavelet transform domain of the cover image. This algorithm takes advantages of the multiresolution feature of wavelet transform and non-relevant feature of the cryptographic random signal. The cryptographic random sequences are generated by the elliptic curve group and Galois Field function selected. The experimental results demonstrate that the scheme proposed is security, invisible and robust against commonly image processing techniques.
This paper offers an introduction of computer assemble and simulation of ancient building. A pioneer research work was carried out by investigators of surveying and mapping describing ancient Chinese timber buildings by 3D frame graphs with computers. But users can know the structural layers and the assembly process of these buildings if the frame graphs are processed further with computer. This can be implemented by computer simulation technique. This technique display the raw data on the screen of a computer and interactively manage them by combining technologies from computer graphics and image processing, multi-media technology, artificial intelligence, highly parallel real-time computation technique and human behavior science. This paper presents the implement procedure of simulation for large-sized wooden buildings as well as 3D dynamic assembly of these buildings under the 3DS MAX environment. The results of computer simulation are also shown in the paper.
This article introduces the multivariate change detection which is based on the established canonical correlation analysis. It also proposes using post processing of the change detected by the multivariate change detection variables using maximum autocorrelation factor analysis. Differing from traditional schemes, the strategy takes two multivariate satellite images covering the same geographic area acquired at different points in time as a random whole sample and transforms two sets of random variables into one set of new random multivariate by using the so-called canonical transformation introduced in the paper. In doing so, the correlation between spectral bands in the same image and in the two different images is removed out as much as possible that the actual changes in all channels simultaneously can be accurately detected. The strategy is invariant to linear scaling. Therefore, it is insensitive to differences in gaining settings in a measuring device, or to linear radiometric and atmosphere correction schemes. The experimental results show the fact that the presented method is exactly creditable and effective on multivariate change detection of remote sensing satellite data.
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