تعيين ماشين¬هاي بردار پشتيبان بهينه در طبقه¬بندي تصاوير فرا طیفی بر مبناي الگوريتم ژنتيک
محورهای موضوعی : عمومىفرهاد صمدزادگان 1 , حديثه سادات حسني 2
1 - دانشگاه تهران
2 - دانشگاه تهران
کلید واژه: ماشین‌های بردار پشتيبان, تصاوير فرا طیفی, طبقه بندي, انتخاب مدل, انتخاب ويژگي, الگوريتم ژنتيک,
چکیده مقاله :
امروزه تصاوير فرا طیفی به علت غناي اطلاعات طيفي يک ابزار قوي و کارامد در سنجش از دور به حساب مي آيند و امکان تمايز بين عوارض مشابه را فراهم مي آورند. با توجه به پايداري ماشین‌های بردار پشتیبان در فضاهايي با ابعاد بالا، یک گزينه مناسب در طبقه بندي تصاوير فرا طیفی محسوب مي شوند. با اين وجود، عملکرد این طبقه بندي کننده ها تحت تأثیر پارامترها و فضاي ويژگي ورودي آن ها مي باشد. به منظور استفاده از ماشين هاي بردار پشتيبان با بيشترين کارایی، مي بايست مقادير بهينه ي پارامترها و همچنين زير مجموعه بهينه از ويژگي هاي ورودي تعيين گردند. در اين تحقيق از توانايي الگوريتم ژنتيک به عنوان يک تکنيک بهينه سازي فرا ابتکاري، در تعيين مقادير بهينه پارامترهاي ماشين هاي بردار پشتيبان و همچنين انتخاب زيرمجموعه ويژگي هاي بهينه در طبقه بندي تصاوير فرا طیفی استفاده شده است. نتايج عملي از به‌کارگیری روش فوق در خصوص داده هاي فرا طیفی سنجنده AVIRISنشان مي دهند، ويژگي هاي ورودي و پارامترها هر کدام جداگانه تأثیر بسزايي بر عملکرد ماشين هاي بردار پشتيبان دارند ولي بهترين عملکرد طبقه-بندي کننده با حل همزمان آن دو بدست مي آيد. در حل همزمان تعيين پارامتر و انتخاب ويژگي، براي کرنل گوسين و پلي نوميال به ترتيب 5% و 15% افزايش دقت با حذف بيش از نيمي از باندهاي تصوير حاصل شد. همچنين الگوريتم بهينه سازي شبيه سازي تبريد تدريجي به منظور مقايسه با الگوريتم ژنتيک پياده سازي شد که نتايج حاکي از برتري الگوريتم ژنتيک به ويژه با بزرگ و پيچيده شدن فضاي جستجو در رويکرد حل همزمان تعيين پارامتر و انتخاب ويژگي مي باشد.
۱٬۳۸۵ / ۵٬۰۰۰ Today, hyperspectral images are considered a powerful and efficient tool in remote sensing due to the wealth of spectral information and provide the possibility of distinguishing between similar complications. Considering the stability of support vector machines in spaces with high dimensions, they are considered a suitable option in the classification of hyperspectral images. Nevertheless, the performance of these classifiers is influenced by their input parameters and feature space. In order to use support vector machines with the highest efficiency, the optimal values of the parameters and also the optimal subset of the input features should be determined. In this research, the ability of the genetic algorithm as a meta-heuristic optimization technique has been used in determining the optimal values of support vector machine parameters and also selecting the subset of optimal features in the classification of hyperspectral images. The practical results of applying the above method on the hyperspectral data of AVIRIS sensor show that the input features and parameters each have a great effect on the performance of support vector machines, but the best performance of the classifier is obtained by solving them simultaneously. In the simultaneous solution of parameter determination and feature selection, for Gaussian kernel and polynomial, 5% and 15% increase in accuracy was achieved by removing more than half of the image bands. Also, the gradual cooling simulation optimization algorithm was implemented in order to compare with the genetic algorithm, and the results indicate the superiority of the genetic algorithm, especially with the large and complicated search space in the simultaneous solution approach of parameter determination and feature selection.
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