کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8866341 | 1620997 | 2018 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Image quality restoration framework for contrast enhancement of satellite remote sensing images
ترجمه فارسی عنوان
چارچوب ترمیم تصویر با کیفیت برای افزایش کنتراست تصاویر ماهواره ای سنجش از دور
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کلمات کلیدی
سنجش از دور، تضاد، ترمیم، کیفیت،
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
چکیده انگلیسی
Researches in satellite remote sensing images mainly revolves around enhancement of contrast and removal of noise in image, which affects the data comprehensibility and clarity. Hence, it is always a challenge to process the satellite remote sensing images in order to obtain better quality images with enhanced visibility and minimum image artifacts for improving their application value. In this paper, an effective quality enhancement framework is proposed, which mainly focuses on contrast enhancement of satellite remote sensing images. Several satellite remote sensing images were tested to ratify the effectiveness of the proposed method over other existing remote sensing enhancement methods and their quantitative results are borne out by NIQMC (No Reference Image Quality Metric for Contrast distortion), BIQME (Blind Image Quality Measure of Enhanced images), MICHELSON (Michelson Contrast), DE (Discrete Entropy), EME (Measure of enhancement) and PIXDIST (Pixel distance) along with qualitative results comparison. Results depict that the visual enhancement obtained using the proposed method is superior to other existing enhancement methods. Finally, the simulation results unveil that proposed method is effective and efficient for satellite remotes sensing images.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing Applications: Society and Environment - Volume 10, April 2018, Pages 104-119
Journal: Remote Sensing Applications: Society and Environment - Volume 10, April 2018, Pages 104-119
نویسندگان
Shilpa Suresh, Devikalyan Das, Shyam Lal, Deep Gupta,