ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2014 | Volume:2 | Issue:1|Papers Count:6

Generalization is a prevalent concept in Cartography to which has been added new aspects such as model generalization with developments in GIS. Increasing demand for tailored and ubiquitous geospatial services like wayfinding, makes the context-aware generalization a noticeable research area in GI Science. Most of the wayfinding services use the network data model as the main spatial data model for their analyses. Whatever data or information that characterizes the situations relevant to users, systems and applications, can be considered as context. A context-aware service is as a service which can sense user, environment and device’s situations and respond to user requests concerning contexts to fulfill the user’s needs better. From the GI services perspective, the context of a query could be the location of the device, environmental settings that query is made in, the time, the activity of the user, user’s personal information, the user’s favorites and information needs, the user’s cognitive map of environment, the mode of travel, the purpose of travel and the device’s technological specifications.In this paper, we try to propose and implement a method for context-aware network generalization at the analysis level. For finding the best path, user contexts like user’s favorite streets will be used. These contexts are modeled as edge’s attributes and those edges which fulfill user’s needs, will be the generators of the Network Voronoi Diagram. With these diagrams, the network will be simplified into sub graphs using Delaunay diagram over the network. The path would be composed of the path between origin and destination to their corresponding generators and the path between generators. This method guarantees the maximum use of edges with user’s need context as well as decreasing computational cost.

Relative Radiometric Normalization is often required in remote sensing image analyses particularly in the land cover change detection process. The normalization process minimizes the radiometric differences between two images caused by inequalities in the acquisition conditions rather than changes in surface reflectance. In this paper a new automatic Relative Radiometric Normalization (RRN) method is proposed which uses an Artificial Neural Network (ANN) and unchanged pixels. The proposed method includes the following stages: 1) automatic detection of unchanged pixels based on a new idea that uses CVA method, PCA transformation and K-means clustering technique, 2) evaluation of different architectures of perceptron neural networks in order to find the best architecture for this specific task and 3) use of the aforementioned network for normalizing the subject image. The method has been implemented on two paires of reference and subject images taken by the TM sensor. Normalization results obtained from the proposed method compared with the 8 conventional methods includes: Histogram matching, Haze Correction, Minimum-Maximum, Mean-Standard deviation, Simple Regression, Linear, Quadratic and Cubic Simple Regression Using Unchanged pixels and Multi Line Regression Using Unchanged Pixels. Experimental results confirm the effectiveness of the presented technique in the automatic detection of unchanged pixels and minimizing any imaging condition effects (i.e., atmosphere and other effective parameters). The proposed method for automatic change detection shows a high capability in detection of changes in covered vegetation areas. Using of this proposed method improves normalization results in all bands, especially in the third and fourth bands which are located in the red and infrared portion of the electromagnetic spectrum. The evaluation results of modeling stage reveal that the normalization using ANN in all 6 bands of all images has produced the more optimum results compared to those of normalization with conventional methods.

Today, depth mapping of coastal and waterfront areas is necessary for various aims such as shipping, dredging, underwater piping, hazardous area detection, hydrological studies, material mapping of water bed, information collection from marine settlements for environment preservation, military and engineering applications. Periodical depth mapping of wide water areas by classic hydrographic method (via ecosounder) is expensive and time consuming. Therefore, due to high capability of remote sensing in rapid data collection from wide area, it can be an effective and proper complementary method for this purpose. Attention to this issue is more important for our country which has long shorelines. At this paper two physical and mathematical bathymetric methods are evaluated. The first method is based on physical behaviour of light attenuation in water column while the second method is a numerical fitting between image gray levels and according water depths by means of artificial neural network (ANN). Our initial experiments show that although the first method has physical meaning but the second method is more accurate and simpler too. Both methods require a set of known hydrographic depthes as calibration data. Therefore, our next experiments try to answer two principal questions: how much can reduce the hydrographic filed operations in remote sensing bathymetry and how much is the accuracy of waterbed topography extracting from satelite images? The result of our experiments showes that introducing of only one hydrographic line perpendicular to coastline as calibration data to ANN method is able to produce satisfied result with depth accuracy RMSE 1.6m and correlation coefficient 92%.

At present, applying the acoustic approaches and equipments has been increasingly used for gathering information from seafloor to produce maps with numerous applications. Multi-beam echo-sounder (MBES) is an acoustic system that measures both the depth and the backscatter strength, simultaneously. Such systems transmit a series of beams to the seafloor and receive their backscatter strengths (BS). They can be described as a function of the incident angle based on the Lambert law. Seafloor sediment classification is an important application of these data. In different studies, angular dependence has been used for this purpose. A complication occurs when the sediment types change along the swath, because it is difficult to separate from the beam angular dependence and the true backscatter strength variations. Therefore, these data should become independence of the angle. In this paper, two statistical methods based on the histogram matching are compared for removing the angular dependence: 1) histogram matching using the statistical moments, and 2) histogram matching using the image processing technique. These methods will register the BS data of large grazing angles (close to nadir) to the reference low grazing angle. Finally we will apply the two methods to a MBES data set of the Waal river, the Netherlands and compare the results.

Measuring water temperature is an important environmental index in order to study narrow channels, rivers, and lakes. Also, it has important role in environmental and water resources management. Although high resolution data can provide better estimation of surface water temperature, but due to their low temporal resolution, they have less applicability. In contrast, low spatial resolution data like Modis images, due to their high temporal resolution (several times a day), is more suitable for estimating parameters of dynamic phenomenon such as water temperature. In order to use low resolution images, a sub-pixel unmixing technique was developed and tried on Urmia lake waters. This approach leads an improvement in accuracy of water temperature maps derived from vectors water features. After derivation of each image fractions for water and land and calculation of emissivity of each fraction, water temperature was estimated using Split Window algorithm. Then temperature water sub-pixel was compared to the temperature map obtained from ASTER thermal bands. Standard error between the images was estimated to be 0.58 centigrade, which is a favorable result.

Surveying has great improvements in data collection techniques in last decade.one of these techniques is laser scanner. with that method we can collect 3D datas automaticly. investigating of the error sources in TLS measurments is rather complicated due to a large number of influencing factors that are quite interrelatred. Thus calibration is an important issue in these devices. several models have been proposed to improve the accuracy of the laser scanners datas until now. each of these models includes some physically parameters and some empirically parameters which have been produced by observation of residuals diagram, in this paper a parametric model based on the internal structure of laser scaner is presented for calibrating these devices. This model compared with another models shows that due to having just physical parameters and not empirical parameters it can be used for a variety of TLS instruments. because of the importance of stability of parameters in a model, stability of them and the correlation between them will be investigated precisely. the results show that this model with a relative stability can improve the accuracy of TLS data.