Change detection is an important process for many applications such as monitoring the effects of environmental hazards, landslides, rock fall and city development projects. RGB images are commonly used as commercial sources of data for monitoring changes visually because they have powerful descriptive information for different features. Automation of detected changes from two RGB images is a challenge because the two images are usually captured in different environments, as temperature, sun angle, clouds, capturing time …etc. The objective of this research is to introduce an automated technique for detecting changes from RGB image based on color channels. The image pixel is represented as a set of its color channels values, R, G and B which is called color signature of image pixel. A real data is used to fulfil the research objective without pre-knowledge about the changes. The correlation coefficients are calculated between color signatures of each two associated pixels from two different registered high resolution satellite images for the same area of study. The detected pixels of changes are identified based on specific correlation value that is identified based on degree of change sensitivity. The degree of sensitivity is based on the importance of detection procedure that is considered as a main part of decision making for risk and crisis management system. The proposed technique is unsupervised and fully automated. It can be applied through a real time process based on the processing capabilities, size and resolution of input images. This technique is easy to use and gives accurate results with neglecting the effects of atmospheric effects.
Hydrological models were applied to extract watersheds and to investigate rainfall computations. There are different techniques for hydrological terrain models. Watershed Modeling System (WMS) and ARC-GIS are commonly used software to simulate and analyse terrain model for extracting watersheds. This research paper applied hydrological models for using two techniques in WMS and ARC-GIS software and extracted a comparative study area between the results of the two different techniques.Storing water in natural or artificial reservoirs are important for barring water to be useful in many applications as electrical, suppress floods, irrigation, human consumption, industrial use, aquaculture, and navigability. The failure of dam structures may cause disasters loss of life when they occur, so dams are considered as installations containing dangerous forces due to the massive impact of a possible destruction on the civilian population and the environment. Failure of dam is a case study for this research paper. A hydrological model is applied to investigate the track of water flow after dam failure of the dam.Available data is 30m Digital Elevation Model (DEM) for the study area, extracted from Shuttle Topographic Rader Mission (STRM). The hydrological mode (HEC l) was applied to simulate surface runoff, watersheds that is used in calculating the amount of water stored behind the dam. The research tested the techniques by using two commonly software, WMS and ARC-GIS.The research investigated the comparative between using water shed modelling system (WMS), (Arc-GIS) to extract watershed, water basin of the study area.The difference between results of the two techniques shows that the calculations by ARC-GIS are more accurate than calculations by WMS.
Airborne LIDAR has become commercially used for many environmental, engineering and civil applications, and can provide accurate data for topographic surfaces and non-terrain objects. Feature extraction is one of the important applications in the field of photogrammetry. This application is used for many civil and military applications-such as photo-interpretation, vegetation, forest monitoring, traffic and transportation development and urban planning. LIDAR Data can have much more dense point spacing than is typically derived from photogrammetry and therefore proper handling of the data is needed to optimal feature extraction. Wavelet transform techniques are widely used as powerful tools in many image processing applications such as de-noising and compression. The discrete wavelet transform is particularly suitable in de-noising and filtering problems. The properties of the wavelet transform, such as having compact support, space and frequency localization, a wide variety of base functions, denoising, thresholding, and multi-resolution analysis, are the main motivations for testing the wavelet transform as an estimation technique for feature extraction from LIDAR data. The major objective of this research is to test the efficiency of wavelet transform in analysing LIDAR data for feature extraction applications. Wavelet transform succeeded in detecting positions of sudden changes of the geometrical or physical content of the images from LIDAR data.
Geometric distortions are common problems when dealing with remote sensing satellite images. Therefore, geometric correction is a necessary process for preparing remote sensing satellite images for many applications. Physical sensor model is formed by integrating the geometry of imaging sensor and positioning sensors as GPS and star trackers with system calibration parameters. Physical sensor model is not available in common but a Rational Polynomial Coefficient (RPC) model is provided as an alternative representation of sensor model. The RPC model is used for geometric correction of satellite image to get the spatial data of image features. The accuracy of the resultant image depends on the accuracy of RPC model which is not known as common. The research objective is to assess the accuracy of geometric correction of the satellite image using RPC model versus geometric correction using Ground Control Points (GCPs), along with their effect on the final accuracy of the output spatial features. The available data is IKONOS-2 image with its RPC file and seven GCPs with high accuracy obtained from ground survey for the study area. The input image is corrected using two available data separately. First degree of polynomial is used for transformation process for the case of GCPs. Bilinear interpolation technique is used to determine the pixel value of the newly resultant corrected images for the two cases. GCPs is preferred when available because the resultant image with RPC geometric correction has 15.0 meters average linear error while 3.0 meters error in case of GCPs geometric correction.
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