استفاده از آنالیزهای مورفولوژی به‌منظور بهبود دقت طبقه‌بندی تصاویر ابر‌طیفی با حد تفکیک بالا

نوع مقاله : علمی - پژوهشی

نویسنده

دانشگاه خواجه نصیرالدین طوسی

چکیده

 آنالیز موفولوژی، با تمرکز بر آنالیز روابط مکانی بین پیکسل‌های همسایه، پردازش تصویر کامل‌تری را در مقایسه با آنالیزهایی که بر پایة اثر طیفی یک پیکسل تنها هستند، به‌دست می‌دهد. روش پیشنهادی در این مقاله با استفادة هم‌زمان از اطلاعات طیفی و اطلاعات مکانی حاصل از آنالیز مورفولوژی نتایج نهایی طبقه‌بندی را در تصاویر ابر‌طیفی بهبود می‌بخشد. در این پژوهش ابتدا با استفاده از نمونه‌های آموزشی محدود، ویژگی‌های منتخب اولیه استخراج شدند و پس از اعمال آنالیزهای مورفولوژی روی هر یک از آنها، پروفایل‌های مورفولوژی تشکیل شدند و از ترکیب این پروفایل‌ها، پروفایل مورفولوژی گسترده تولید شد. سپس پروفایل مورفولوژی گسترده‌شده با ویژگی‌های منتخب اولیه ترکیب شد و مجدداً استخراج ویژگی نهایی صورت گرفت. ویژگی‌های منتخب نهایی با استفاده از طبقه‌بندی‌کنندة ماشین‌ بردار پشتیبان طبقه‌بندی شدند. سپس پس‌پردازش تصویر نهایی با استفاده از فیلتر رأی‌گیری اکثریت انجام شد. این روش، روی دادة شهری و نیمه‌شهری از سنجندة ROSIS تست شد. دقت طبقه‌بندی نهایی از 86/98 و 70/82 درصد در روش‌های معمولی به 36/99 و 75/95 درصد در روش پیشنهادی به‌ترتیب در تصویر منطقة شهری و نیمه‌شهری افزایش یافته است.  کلید‌واژه‌ها: آنالیز مورفولوژی، ماشین‌بردار پشتیبان، استخراج ویژگی، طبقه‌بندی، رأی‌گیری اکثریت.   

کلیدواژه‌ها

عنوان مقاله [English]

Using of Morphology Analysis for Precision Improvement of High Resolution Hyper Spectral Images Classification

چکیده [English]

Morphology analysis which concentrates on spatial relations analysis between neighborhood pixels provides a better image processing compared to analyses which are only based on spectral signature of a single pixel. The proposed method in this paper integrates spectral and spatial information produced from morphology analysis to improve the final result of hyper spectral image classification. For this reason at first, primary components are extracted using limited training samples. Extended morphological profiles are then produced by applying morphological analysis on each extracted features. Afterwards, Final components are extracted by applying a supervised feature selections on a datasets composed of both the spectral and the extended morphological features. The extracted features are introduced into the Support Vector Machine (SVM) algorithm. The final results are then archived by implementing a majority filter as a post-processing step. The proposed method is implemented on aerial hyper spectral images of Rosis sensor taken from urban and semi-urban areas from. The obtained results proved the efficiency of the proposed method where classification accuracies are improved from 98.86 and 82.70 in conventional method to 99.36 and 95.75 in urban and semi-urban areas respectively. Keywords: Morphological Analysis, Support Vector Machines (SVMS), Feature Extraction (FE), Classification, Majority Vote

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