کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
506907 865066 2016 9 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient
ترجمه فارسی عنوان
تنظیم بلوک بسته نرم افزاری داده های سنجش از راه دور در مقیاس بزرگ با فشرده سازی ماتریس پراکنده مبتنی بر بلوک ترکیب شده با گرادیان کونژوگه پیش شرط
کلمات کلیدی
تنظیم بلوک بسته نرم افزاری ؛ داده های سنجش از راه دور در مقیاس بزرگ ؛ فشرده سازی ماتریس پراکنده مبتنی بر بلوک ؛ گرادیان کونژوگه پیش شرط
Bundle block adjustment; Large-scale remote sensing data; Block-based Sparse Matrix Compression; Preconditioned Conjugate Gradient
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• A stable and efficient bundle block adjustment system to deal with large-scale remote sensing data is built.
• The BSMC method is introduced to combine with PCG aiming to decrease the memory requirement.
• PCG algorithm is applied to solve the large normal equation.

In recent years, new platforms and sensors in photogrammetry, remote sensing and computer vision areas have become available, such as Unmanned Aircraft Vehicles (UAV), oblique camera systems, common digital cameras and even mobile phone cameras. Images collected by all these kinds of sensors could be used as remote sensing data sources. These sensors can obtain large-scale remote sensing data which consist of a great number of images. Bundle block adjustment of large-scale data with conventional algorithm is very time and space (memory) consuming due to the super large normal matrix arising from large-scale data. In this paper, an efficient Block-based Sparse Matrix Compression (BSMC) method combined with the Preconditioned Conjugate Gradient (PCG) algorithm is chosen to develop a stable and efficient bundle block adjustment system in order to deal with the large-scale remote sensing data. The main contribution of this work is the BSMC-based PCG algorithm which is more efficient in time and memory than the traditional algorithm without compromising the accuracy. Totally 8 datasets of real data are used to test our proposed method. Preliminary results have shown that the BSMC method can efficiently decrease the time and memory requirement of large-scale data.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Geosciences - Volume 92, July 2016, Pages 70–78
نویسندگان
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