Classification of Land Cover in Satellite Image using Supervised and Unsupervised Techniques
نویسندگان
چکیده
منابع مشابه
Remote Sensing Image Fusion for Unsupervised Land Cover Classification
1.1 Context With the development of new satellite systems and the accessibility of data from public through web services like Google Earth, remote sensing imagery, knows today an important growing which advanced and still advances researches in this area on different aspects. Especially in cartography, many studies have been conducted for multi-source satellite images classification. These stud...
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با توجه به گسترش روز افزون تقلب در حوزه بیمه به خصوص در بخش بیمه اتومبیل و تبعات منفی آن برای شرکت های بیمه، به کارگیری روش های مناسب و کارآمد به منظور شناسایی و کشف تقلب در این حوزه امری ضروری است. درک الگوی موجود در داده های مربوط به مطالبات گزارش شده گذشته می تواند در کشف واقعی یا غیرواقعی بودن ادعای خسارت، مفید باشد. یکی از متداول ترین و پرکاربردترین راه های کشف الگوی داده ها استفاده از ر...
Land Cover Classification of Satellite Images Using Contextual Information
This paper presents a method for the classification of satellite images into multiple predefined land cover classes. The proposed approach results in a fully automatic segmentation and classification of each pixel, using a small amount of training data. Therefore, semantic segmentation techniques are used, which are already successful applied to other computer vision tasks like facade recogniti...
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Land use and Land Cover Classification using RGB&L Based Supervised Classification Algorithm
After the Geometric correction and resampling the image should be classified. This paper introduced a new method for classifying the areas in a remotely sensed image under the category of supervised classification techniques. This classification technique describes how to classify the geographical areas in given image under supervised classification techniques conventions. So to tell the abstra...
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ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2016
ISSN: 0975-8887
DOI: 10.5120/ijca2016908382