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Article
Detecting and Monitoring the Vegetal Cover of Karbala Province (Iraq) Using Change Detection Methods
كشف ومراقبة الغطاء النباتي لمحافظة كربلاء (العراق) باستخدام طرق كشف التغيرات

Authors: Israa J. Muhsin اسراء جميل محسن --- Amjed H. Mohammed امجد حامد محمد
Journal: Iraqi Journal of Science المجلة العراقية للعلوم ISSN: 00672904/23121637 Year: 2017 Volume: 58 Issue: 3A Pages: 1345-1354
Publisher: Baghdad University جامعة بغداد

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Abstract

Karbala province was one of the most important areas in Iraq and considered an economic resource of vegetation such as trees of fruits, sieve and other vegetation. This research aimed to utilize change detection for investigating the current vegetation cover at last three decay. The main objectives of this research are collect a group of studied area (Karbala province) satellite images in sequence time for the same area, these image captured by Landsat (TM 1995, ETM+ 2005 and Landsat 8 OLI (Operational Land Imager) 2015. Preprocessing such as atmosphere correction and rectification has been done. Mosaic model between the parts of studied area was performing. Gap filling consider being very important step has been implied on the defected image which captured in Landsat 2005. For monitoring the changes in the studied area, many image processing such as supervised classification using Maximum likelihood classifier and support vector machine classifier have been applied. Target detection using matching filter and change detection using subtractive method also have been used to detect the change in vegetal cover of the studied area. Many histogram and statistical properties were illustrated as well as the pixel count and the target area has been computed.

تعتبر محافظة كربلاء واحدة من اهم المدن الزراعية في العراق وتعتبر من اهم الموارد الاقتصادية للنباتات مثل اشجار الفاكة والنخيل والاشجار الاخرى. يهدف بحثنا الحالي الى استخدام تقنيات كشف التغييرات في مراقبة الغطاء النباتي لمحافظة كربلاء للعقود الثلاث الاخيرة. ان الهدف الرئيسي من هذا البحث جمع مجموعة من الصور الفضائية لمنطقة الدراسة في اوقات مختلفة لنفس المنطقة, هذة الصور اخذت بواسطة القمر الصناعي لاندسات (TM 1995, ETM+ 2005 and Landsat-8 OLI 2015). وقد تم اجراء مجموعة من التصحيحات على الصور الملتقطة منها تصحيح تأثيرات الغلاف الجوي. وكذلك تم استخدام تقنية الفسيفساء لدمج اجزاء الصورة الخاصة بمنطقة الدراسة. كما تم ترميم الصورة المشوهة الملتقطة بالقمر لاندسات ETM+7 لعام 2005 باستخدام قيمة المعدل لملأ الفراغات في الصورة. تضمن هذا البحث مجموعة من تقنيات المعالجة الصورية لمراقبة التغيرات في الغطاء النباتي لمنطقة الدراسة من هذه التقنيات تصنيف الصور مثل التصنيف الموجة باستخدام طريقة (Maximum likelihood classifier and support vector machine classifier). وكذلك تم استخدام طريقة مرشحات المطابقة لكشف الاهداف كما تم استخدام طريقة طرح الصورمن بعضها لكشف التغير بالغطاء النباتي للمنطقة الدراسة. تم استخراج العديد من الاشكال البيانية والاحصائية للنتائج كما تم حساب عدد البكسلات للهدف بالاضافة الى مساحة الجزء النباتي من منطقة الدراسة.


Article
Adaptive Hyper Classification Technique for Satellite Images

Authors: Hazeem B. Taher --- Najlaa M. Mohie
Journal: Journal of Education for Pure Science مجلة التربية للعلوم الصرفة ISSN: 20736592 Year: 2019 Volume: 9 Issue: 2 Pages: 32-41
Publisher: Thi-Qar University جامعة ذي قار

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Abstract

Abstract:The satellite image classification system intends to differentiate between the objects being present in theimage. It is highly challenging because the coverage area of the satellite is large such that the objects appear so small.This makes the process of object differentiation complex. Additionally, the classification accuracy is an importantfactor, which the classification system must pass through. This work presents a satellite image classification systemwhich can classify between the vegetation, soil and water bodies, etc. Current research work is in the developmentof a hybrid classification technique for satellite images. High-resolution satellite images that we deal with usingSVM and low accuracy through the use of k-means. It is necessary to provide accuracy and speed of satellite imagesand obtain this through the modulation method suggested through the integration of these technicians, wheretechnology can classify all satellite images .We note that the proposed system is able to classify any satellite imageby highlighting each region and what indicates that area and what percentage when applying the proposed system ofclassification whether the satellite images are based on high accuracy or low accuracy, where the system also showsthe time taken to classify each satellite image. We note that the time does not exceed 30 seconds for the number ofimages used within our database

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