يعد التقطيع الصوري من الاهداف الرئيسة والضرورية في المعالجات الصورية للصور الرقمية، فهو يسعى الى تجزئة الصور المدروسة الى مناطق متعددة اكثر نفعاً تلخص فيها المناطق ذات الافادة لصور الاقمار الصناعية، وهي صور متعددة الاطياف ومجهزة من الاقمار الصناعية باستخدام مبدأ الاستشعار عن بعد والذي اصبح من المفاهيم المهمة التي تُعتمد تطبيقاته في اغلب ضروريات الحياة اليومية، وخاصة بعد التطورات المتسارعة التي شهدتها ميادين الحياة المختلفة والتي كثيراً منها طرقت بابها خوارزميات وتقنيات البرمجيات، فهذه الصور تعد ضرورية جداٌ لتمكيننا من دراسة طيف واسع من الاهداف في العديد من الجوانب العلمية، في هذا البحث استخدمت خوارزمية التحليل العنقودي غير الهرمية كطريقة للتقطيع الصوري (شطر ودمج المناطق) بهدف عرض اهمية استخدام الاساليب الاحصائية في مهام المعالجة الصورية مثل التقطيع الصوري، حيث اعتمد على تقنية (K-Means) لتنفيذ هذه المهمة، وقد طبقت خوارزمية هذه التقنية على صورة اقمار صناعية متعددة الاطياف لمشهد غربي العراق، حيث اظهرت النتائج مدى مرونة هذه الخوارزمية في التعامل مع التفاوت في اضاءة العناصر الصورية للصورة الملونة وكفاءة تكوينها لمناطق العناقيد المتكونة من مجاميع من العناصر الصورية المتجانسة في درجة شدة اضاءتها، واخيراً قدرة هذه الخوارزمية على اعطاء صور تتميز بجودتها والتي قيست على وفق مقياس ارتفاع اشارة نسبة الضوضاء (Peak Signal to Noise Ratio (PSNR)) لقياس جودة الصورة.
LK Abood, RA Ali, M Maliki, International Journal of Science and Research, 2015 - Cited by 2
This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio
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Regression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent mechanism to attain optimality while reducing or prioritizing test cases. Nevertheless, those studies were deprived of systematic procedures to manage tied test cases issue. Moreover, evolutionary algorithms such as the genetic process often help in depleting test cases, together with a concurrent decrease in computational runtime. However, when examining the fault detection capacity along with other parameters, is required, the method falls sh
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