This research registers aerial photos and oblique helicopter images onto building facades. In which, building roofs and walls are to be treated. In addition to geometric registration, the proposed method detects hidden areas for roofs and walls, then compensates them with multi-view images and inpainting techniques. The wall images that we choose are based on oblique photography. For the target wall, we should select the largest oblique angle for the prior image in order to get better resolution. However, the occlusion problem by neighboring buildings is more serious. It is the major task of this study to select the optimal combination of images. For the roof, we use the vertical photos to obtain better resolution. The essence of the step is to register the roof surface with images. The results show high fidelity in visualization.

The geometric modeling may be divided into two categories, namely, rigorous sensor model (RSM) and rational function model (RFM). The RSM contains Bundle Adjustment and Direct Georeferencing. Actually, some satellite companies provide the Rational Polynomial Coefficients (RPC) instead of the ephemeris data. Some others are on the contrary. The adjustment of the heterogeneous models between RSM and RFM should be integrated when the two types of images are employed.This paper combines Direct Georeferencing and RFM for multi-sensor block adjustment. Two heterogeneous models using Digital Elevation Model (DEM) as elevation control are integrated. The major works of the proposed schemes include (1) building up Direct Georeferencing mathematics, (2) setting up RFM, and (3) compensating the local systematic errors by least squares collocation. The experiments test the different geometric models, different numbers of tie points, and the integration of multi-resolution images. The validation includes two parts: absolute accuracy and relative discrepancy. Experimental results indicate that the proposed method can improve the relative discrepancy.

Generally, the global navigation satellite system carrier-phase is more accurate then the pseudorange. The key point is how to obtain the correct integer ambiguity quickly and efficiently, while using carrier-phase for positioning. However the high correlation between parameters makes it to be difficult. The problem can be improved by the changing of the geometric of satellites. But it needs longer observation time to reach.The LLL algorithm is a technique mapping the parameters from a higher correlation space to a lower correlation space. The LLL algorithm uses the Gram-Schmidt orthogonalization to produce the diagonal covariance matrix. The effects of mathematics changing and the geometric changing can be the same. The number of candidates for integral ambiguity can reduce by using the diagonal covariance matrix. Final, the candidates are inserted into the observation equations to determine the solution again. It is believed that the integer candidate which produces the smallest sum of squares of the residual is the most likely solution we want

This story uses a two unsupervised fuzzy and probabilistic clustering method in order to research how predict the space of rivers is polluted. The river pollution includes many kinds of different water quality. But it is to need to possess various and representative study sample to utilize the supervised type to classify. Unsupervised method does not needing to study samples, but need local value as monitoring and classifying the basis finally. This method improves many traditional unsupervised methods that have been set up the number of clusters, and the automatic optimization classification. It is classing the pollution of river in the image of the satellites. In addition improve the accuracy predicted to the pollution of river, input the best variable association that is screened, in order to set up the prediction system that the space of river pollution in the image of the satellites.

This study extended traditional GLCM (Gray Level Co-occurrence Matrix) texture analysis to a tensor field (Gray Level Co-occurrence Tensor Field, GLCTF) that uses three voxels from hyperspectral image cubes, and utilized third order statistics to extract subtle features from hyperspectral datasets. This paper also developed an algorithm based on spectral separability measures to identity the best sampling intervals of class pairs as the appropriate kernel sizes in the spectral dimension for GLCTF computation. Combining with spatial window sizes determined from semi-variance analysis, it is more appropriate to determine the size of a three-dimensional moving box for GLCTF computation to extract separable features from hyperspectral datasets for classification. Experimental results demonstrated in this paper indicate that coupling separability measures and semi-variance analysis can determine more appropriate kernel sizes for 3D texture computation. In addition, GLCTF in most indexes has better classification results in the test cases.

Secchi depth affects the performance of the airborne bathymetric lidar system. Therefore, the operation efficiency can be benefited if the secchi depth turbidity can be obtained in advance. In this research, the MODIS sensor onboard Aqua satellite is used due to its ability to provide extensive temporal and spatial imagery. A total of 65 depth profiles are collected including instruments of Secchi Disk, AC-9, and Hydroscat-6 from May, 2007 to May, 2008. Within that dataset, a total of 19 profiles were collected concurrent with Aqua MODIS overpass. There have been reports of three relationships between optical properties and secchi depth based on either 488 nm or 532 nm. Investigations are made to compare which combination of optical relationship and wavelength is most reliable. Temporal analysis of MODIS images reveal a yearly variation of Secchi depth values, which is important information for airborne bathymetric lidar flight planning.