A LEAST SQUARE ALGORITHM FOR GEOMETRIC MATCHING OF REMOTE SENSED IMAGES
نویسندگان
چکیده
منابع مشابه
Fast Least Square Matching
Least square matching (LSM) is one of the most accurate image matching methods in photogrammetry and remote sensing. The main disadvantage of the LSM is its high computational complexity due to large size of observation equations. To address this problem, in this paper a novel method, called fast least square matching (FLSM) is being presented. The main idea of the proposed FLSM is decreasing t...
متن کاملA Remote Sensing Image Matching Method Combining Genetic Algorithm with Least Square Matching
Image matching is an important task in digital photogrammetry. In the paper, an approach to remote sensing image matching combining genetic algorithm(GA) with least square matching(LSM) is presented to speed up image matching and provide a robust reliable and rather accurate initial value for high-precision subpixel matching. The experiment show that the matching method based on GA is much fast...
متن کاملinvestigating the feasibility of a proposed model for geometric design of deployable arch structures
deployable scissor type structures are composed of the so-called scissor-like elements (sles), which are connected to each other at an intermediate point through a pivotal connection and allow them to be folded into a compact bundle for storage or transport. several sles are connected to each other in order to form units with regular polygonal plan views. the sides and radii of the polygons are...
Least Mean Square Algorithm
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...
متن کاملLeast Mean Square Algorithm
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
سال: 2020
ISSN: 2194-9050
DOI: 10.5194/isprs-annals-v-2-2020-121-2020