The establishment of high quality triangular mesh is one of the key steps in 3D laser scanning data processing.
Traditional triangulation algorithms have been proposed directly on the basis of adjacency relation between points in
3D space. However, when the point density is non-uniform or the noise exists, the problems such as surface hole,
dough sheet overlapping and inconsistent normal appear easily. In this paper, a triangular mesh establishing algorithm
based on ellipsoidal projection is proposed. After comparing the theory of ellipsoidal projection and cylindrical
projection, the proposed triangular mesh establishing algorithm is analyzed in detail including basic idea and
implementation method. To evaluate the performance and efficiency of the proposed algorithm, two experiments are
then carried out on the 3D point cloud data of a foundation pit. The results indicate that though the computational
efficiency of proposed algorithm is a little inferior to the algorithm based on cylindrical projection, the proposed
algorithm is more effective for establishing point cloud of both top and bottom of the object and the original topological
relation of 3D scanning points can be maintained better.
Classic Kalman Filter is a dynamic and efficient data processing method, but there are some limitations. Robust
estimation theory will be introduced to the Classical Kalman Filter (CKF) method, that is: Robust Adaptive Kalman
Filter (RAKF). There is a clear advantage in reducing the observational errors and the state prediction errors context. In
this paper, it uses a dam deformation monitoring example to illustrate that the RAKF is more reliable than the CKF in the
deformation monitoring data processing effectively, and it is obviously in inhibiting the aspect of the state prediction
errors and the observational errors. It is a viable and effective method of estimation method.
Ideally, one objective of image fusion in remote sensing is to obtain high-resolution multispectral images with simultaneously the spectral characteristic of multispectral images and an enhanced spatial resolution. To date, numerous image fusion techniques have been developed. However, many methods may introduce spectral distortion, appearing as a change in colors between compositions of resampled and fused multispectral bands. To tackle this problem, some methods have taken the radiometric characteristics of sensors into account. This paper is an attempt to fuse high-resolution panchromatic and low-resolution mutlitspectral bands of the EO-1 ALI sensor. Starting from the analysis of spectral difference between ALI and other sensors, the authors present two methods which take into account the physical spectrum response of sensors during the fusion process: one is an improved fast intensity-hue-saturation (IHS) method with spectral adjustment according to sensor spectral response, and the other directly introduces sensor spectral response into the general component substitution image fusion method. An experiment based on ALI images has been carried out to demonstrate the effectiveness of the proposed approach. The fused images processed through the proposed methods have almost the same spatial resolution as panchromatic images and keep good spectral characteristics.