Change detection is a technology ascertaining the changes ofspecific features within a certain time Interval. The use of remotelysensed image to detect changes in land use and land cover is widelypreferred over other conventional survey techniques because thismethod is very efficient for assessing the change or degrading trendsof a region. In this research two remotely sensed image of Baghdadcity gathered by landsat -7and landsat -8 ETM+ for two time period2000 and 2014 have been used to detect the most important changes.Registration and rectification the two original images are the firstpreprocessing steps was applied in this paper. Change detection usingNDVI subtractive has been computed, subtractive between the bandsof the two images and the ratio of the red to blue bands was alsocomputed. Change detection mask using minimum distanceclassification or detection after classification have be also used tocompute the changes between the resultant classes, many statisticalproperties of the original and process image have been illustrated inthis research
Digital change detection is the process that helps in determining the changes associated with land use and land cover properties with reference to geo-registered multi temporal remote sensing data. In this research change detection techniques have been employed to detect the changes in marshes in south of Iraq for two period the first one from 1973 to 1984 and the other from 1973 to 2014 three satellite images had been captured by land sat in different period. Preprocessing such as geo-registered, rectification and mosaic process have been done to prepare the satellite images for monitoring process. supervised classification techniques such maximum likelihood classification has been used to classify the studied area, change detection after classification have been implemented between the new classes of adopted images, and finally change detection using matched filter was applied on the region of interest for each class.
Ground Penetrating Radar (GPR) is a nondestructive geophysical technique that uses electromagnetic waves to evaluate subsurface information. A GPR unit emits a short pulse of electromagnetic energy and is able to determine the presence or absence of a target by examining the reflected energy from that pulse. GPR is geophysical approach that use band of the radio spectrum. In this research the function of GPR has been summarized as survey different buried objects such as (Iron, Plastic(PVC), Aluminum) in specified depth about (0.5m) using antenna of 250 MHZ, the response of the each object can be recognized as its shapes, this recognition have been performed using image processing such as filtering. Where different filters like (DC adjustment, triangular FIR, delete mean trace, FIR) have been applied on output image as well as the simulation of the soil and the buried objects layers have been obtained using GPR simulation program.
Image retrieval is used in searching for images from images database. In this paper, content -based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level cooccurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is concluded that, for the database images used in this work, the CBIR using hybrid technique is better for image retrieval because it has a higher match performance (100%) for each type of similarity measure so; it is the best one for image retrieval.
Karbala province regarded one part significant zones in Iraq and considered an economic resource of vegetation such as trees of fruits, sieve and other vegetation. This research aimed to utilize Normalized Difference Vegetation index (NDVI) and Subtracted (NDVI) for investigating the current vegetation cover at last four decay. The Normalized Difference Vegetation Index (NDVI) is the most extensively used satellite index of vegetation health and density. The primary goals of this research are gather a gathering of studied area (Karbala province) satellite images in sequence time for a similar region, these image captured by Landsat (TM 1985, TM 1995, ETM+ 2005 and Landsat 8 OLI (Operational Land Imager) 2015. Preprocessing such gap filling consider being vital stride has been implied on the defected image which captured in Landsat 2005 and isolate the regions of studied region. The Assessment vegetal cover changes of the studied area in this paper has been implemented using Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI) and change detection techniques such as Subtracted (NDVI) method also have been used to detect the change in vegetal cover of the studied region. Many histogram and statistical properties were illustrated has been computed. From The results shows there are increasing in the vegetal cover from 1985 to 2015.
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