کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6949256 1451239 2018 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Uniform competency-based local feature extraction for remote sensing images
ترجمه فارسی عنوان
استخراج ویژگی های محلی مبتنی بر شایستگی برای تصاویر سنجش از راه دور
کلمات کلیدی
تطبیق تصویر، آشکارساز ویژگی، توزیع مقیاس و فضا، نیرومندی، سنجش عملکرد،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی
Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 135, January 2018, Pages 142-157
نویسندگان
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