تشخیص و طبقهبندی عوارض در تصاویر Sentinel-2 با استفاده شبکه Resnet
پژوهش های سنجش از دور و اطلاعات مکانی
مقالات آماده انتشار ، پذیرفته شده، انتشار آنلاین از تاریخ 11 خرداد 1404
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22061/jrsgr.2025.11719.1094
نویسندگان
پویا حیدری ؛ اصغر میلان* ؛ علیرضا قراگوزلو
گروه مهندسی نقشه برداری، دانشکده مهندسی عمران آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران
تاریخ دریافت : 15 آذر 1403 ،
تاریخ بازنگری : 26 اردیبهشت 1404 ،
تاریخ پذیرش : 04 خرداد 1404
چکیده
پیشینه و اهداف: امروزه با توجه به استفاده روز افزون از اطلاعات پوشش و کاربری اراضی در کاربردهای مختلف، کسب این اطلاعات امری ضروری میباشد. استفاده از تصاویر سنجش از دوری به عنوان راهکار اصلی کسب این اطلاعات محسوب میشود. برای استخراج پوشش و کاربری اراضی از این تصاویر، میتوان از تکنیکهای طبقهبندی تصاویر بهره برد. با توجه به پتانسیل بالای روشهای یادگیری عمیق در طبقه بندی تصاویر، این روشها میتوانند به طور موثری در طبقهبندی پوشش و کاربری اراضی استفاده شوند. با این حال، استفاده از این روشها همراه با چالشهایی نیز میباشد. یکی از مشکلات اصلی استفاده از روشهای یادگیری عمیق، بیش برازش مدل میباشد. از دیگر معضلات اصلی این روشها میتوان به نیازمند بودن این روشها به تعداد بسیار زیاد داده در مرحله آموزش اشاره نمود. همچنین ناپدید شدن و انفجار گرادیان و انتخاب معماری مناسب از دیگر مشکلات و چالشهای این روشها برای استخراج پوشش و کاربری اراضی از تصاویر سنجش از دور میباشند .
روشها: هدف اصلی این پژوهش استفاده از تکنیکهای مختلف برای رفع این چالشها و رسیدن به دقتهای بالا در انجام طبقهبندی پوشش و کاربری اراضی میباشد. برای مرتفع نمودن چالش بیش برازش مدل، از تکنیکهای حذف تصادفی و توقف زودهنگام استفاده شد تا دقت در دادههای آموزشی و تست نزدیک به یکدیگر باشند. استفاده از روش داده افزایی میتواند کمبود دادههای آموزشی را برطرف نماید و از بیش برازش مدل نیز جلوگیری کند. به همین علت از این روش برای افزایش داده های آموزشی مدل استفاده شد. تکنیک برش گرادیان نیز در این پژوهش استفاده شد تا از انفجار و ناپدید شدن گرادیان در مدلهای یادگیری عمیق جلوگیری کند. معماری استفاده شده در این پژوهش برای طبقه بندی مجموعه داده EuroSat، مدل ResNet18 بوده است.
یافتهها: در ابتدا از این معماری به همراه تکنیک توقف زودهنگام برای انجام طبقه بندی استفاده شد و مدل به دقت کلی 19/91 درصد و ضریب کاپای 9018/0 رسید. سپس به همین مدل تکنیک داده افزایی اضافه شد و مدل به دقت کلی 78/91 درصد و ضریب کاپای 9085/0 دست یافت که نشان میدهد نسبت به مرحله قبلی دقتهای بهتری حاصل شده است. در مرحله آخر تکنیک حذف تصادفی با نرخ 5/0، برش گرادیان با حدآستانه 1/0 نیز به مدل قبلی اضافه شد و مدل به دقت کلی 11/93 درصد و ضریب کاپای 9233/0 رسید که نسبت به دو مرحله قبلی به دقت های بهتری رسیده است.
نتیجهگیری: این نتایج نشان میدهد که دقت طبقهبندی پوشش و کاربری اراضی مجموعه داده EuroSat در مرحله آخر نسبت به مراحل قبلی به دقت بهتری دست یافته است.
کلیدواژهها
طبقهبندی ؛ پوشش و کاربری اراضی ؛ سنجش از دور ؛ یادگیری عمیق ؛ شبکه عصبی پیچشی
عنوان مقاله [English]
Object detection and classification with Sentinel-2 imagery using ResNet
نویسندگان [English]
P. Heidari؛ A. Milan؛ A.R. Gharagozlou
Department of Surveying Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
چکیده [English]
Background and Objectives: Nowadays, getting land cover and land use information is crucial due to the growing number of uses for this data. The primary method for obtaining this information is considered to be through the utilization of remote sensing images. Image classification techniques should be employed so as to extract land cover and use from these images. Deep learning techniques can be utilized effectively to the classification of land cover and land use simply because of their great potential in image classification. But there are also challenges when applying these techniques as well. Model overfitting is one of the most common issues when utilizing deep learning algorithms. Another major issue with these methods is that they demand a significant amount of data during the training stage. Additionally, gradient exploding/vanishing and determining the suitable architecture are further challenges associated with these methods for extracting land cover and use from remote sensing imagery.
Methods: The main objective of this research is to employ different techniques to overcome the challenges to achieve high classification accuracy. To solve the problem of model overfitting, dropout and early stopping approaches were utilized to ensure that the accuracy of the training and test data were close. The data augmentation strategy can prevent model overfitting in addition to addressing the lack of training data. As a result, this method was employed to augment training data and also avoiding model overfitting. The gradient clipping strategy was additionally used in this study to mitigate gradient exploding and vanishings in deep learning models. This study used the ResNet18 model to classify the EuroSat dataset, enabling us to obtain highly effective classification accuracy.
Findings: Initially, this architecture was used with with the early stopping strategy, and the model had an overall accuracy of 91.19 percent and a kappa coefficient of 0.9018. The data augmentation technique was then applied to the same model, and the model achieved an overall accuracy of 91.78 percent with a kappa coefficient of 0.9085, surpassing the previous stage. In the last stage, a dropout method with a rate of 0.5 and a gradient clipping with a threshold of 0.1 were added to the previous model, and the model achieved an overall accuracy of 93.11 percent and a kappa coefficient of 0.9233, which was more accurate than the previous two stages.
Conclusion: These results indicate that the EuroSat's land cover and land use classification accuracy in the final stage was higher than in prior stages.
کلیدواژهها [English]
Classification, Land Cover and Land Use, Remote Sensing, Deep Learning, Convolutional Neural Network
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