ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2014 | Volume:2 | Issue:3|Papers Count:6

In this paper at first some image compression methods such as wavelet, KLT, Discrete Cosine Transform and combined wavelet and KLT were reviewed and then anew method based on multi averaging was proposed. The proposed method was applied on some images including a spaceborn remote sensing image and the results were presented. Few statistical parameters such as MSE, PSNR and CR for evaluation of the method on the compressed images were calculated. In addition, for image retrieving, the averaging compressed image and then minimum and maximum values were used.

Due to the existing restrictions in the global navigation satellite systems like GPS and their daily increasing applications, new generations of satellite navigation systems are being emerged. The new generations of the satellite navigation systems are classified to complementary and regional independent ones. The geometry or the constellation design of a new system depends on the space mission goals and other various parameters. This paper discusses on the design procedure for the geometry of the space segment in a regional satellite positioning and navigation system for Middle East. The proposed constellation is composed of 7 geo-synchronous satellites which provide an optimum coverage in the study area. For this purpose, PDOP, constellation value, internal and external reliabilities as well as the rate of success in ambiguity resolution have been used for selecting the optimum design among the 5 proposed scenarios.

Orbital parameters model is one of the fully constrained physical models for geometrical correction of satellite imagery. The model has been developed to cover the physical conditions prevailing in the acquisition period of satellite platforms. The multiplicity of parameters in the modeling and correlation between them causes difficulties in solving the system of equations. Generally, problems are greatly reduced by adding the model parameters in the form of quasi observations and controlling trend of corrections by the values of each parameter's weights. However, it is difficult to determine the correct values of quasi observations' weight due to the approximate precision of auxiliary data. Furthermore wrong weights of quasi observations impose additional parameters in the model structure. In this article, by providing some scenarios, the level of correlations between orbital model parameters and also the capability of rigid orbital model in the covering effect of perturbations are evaluated. The obtained results proved that unconsidered perturbations, occurring in a specified domain, are coverable by other correlated parameters of rigid orbital parameters model. This ability of the rigid orbital parameters model is more evident when quasi observations are not applied in the model.

The urban environments may have much functionality in different issues that each one depends on different aspects. This paper presents an analytical framework in spatial database systems for urban layouts to study the relations between urban forms and functionalities of urban environments. The selected functionality that is considered in this paper is Responsiveness of urban environments. The method is based on a precise introduction of the concept in a spatial computational procedure. The presented method makes a comprehensive introduction and consists of parameters and their interactions. The applied method has two main components that are the introduction of responsive environments and the spatial database of urban layouts. The mapping of the parameters and relations to objects and attributes spatial database leads to retrieving the function of responsiveness in spatial database. The method uses many spatial and geometrical parameters to measure the urban layout’s components and presents the 16 logical relations between the responsive environments and spatial database components. The implementation is done for district 12, region 4 of the Tehran and the output layers are verified by people’s opinions, land use map of extent and field observations.

A fully polarimetric synthetic aperture radar (POLSAR) image can provide a high-dimensional data. This large amount of information can increase the overall accuracy of land-cover classification. But increasing the data dimensions if inadequately number of training samples may increase the complexity and cause the curse of dimensionality phenomenon. One of the strategies for solving this problem is the use of multiple classifier systems (MCS) that has the capability of divide and conquer to the large data as compared to the individual classifiers. In addition, some of MCS methods using the weak and unstable classifiers such as decision tree (DT) and neural network (NN) can obtain the high accuracy in high-dimensional data. The objective of this paper is also to use several popular MCS methods such as adaboost, bagging and random forests in order to improve the accuracy of land-cover classification from high-dimensional PolSAR images. The data used in this paper are Radarsat-2 image from San Francisco Bay and AIRSAR image of Flevoland. For classifying two these images, 69 polarimetric features were extracted from them. Two classifiers of DT and NN were chosen as the base classifiers of adaboost and bagging methods. In the next, the MCS methods were compared with the individual classifiers of DT and NN. The results indicated the higher overall accuracy of MCS methods between 5%–8% for classifying first image and 9%–16% for classifying second image. Even, the producer's accuracy and user's accuracy of MCS methods at all classes were more than the those of individual classifiers. So that at some classes, the difference was between 20% to even near 50%. These results confirmed that the MCS methods not only can produce higher overall accuracy at land-cover classification, but also they have the higher efficiency and reliability at discriminate individual classes.

Overlapping satellite images are one of the key solutions in both generating and updating of spatial data like digital elevation model (DEM). To do this, rational polynomial coefficients (RPCs), as an essential Meta data for satellite images, are regularly used. Nonetheless, these coefficients due to some systematic errors in their gathering lead to an erroneous DEM. Accordingly, in this study a new method is proposed to make it possible to obtain an accurate DEM using automated bias compensation of the RPCs in object space. In this method, RPC biases on the resulting DEM are nearly removed using a 2.5D matching procedure between IRS-P5 derived DEM and the national-cartographic-center DEM (NCC DEM). The experimental results of this study showed that using both 10-meter NCC DEM and stereo images of IRS-P5 satellite images can achieve about one pixel level of accuracy or better in both DEM generating and updating.