Computational Intelligence in Remote Sensing Image Registration: A survey

Wu, Yue; Liu, Junwei; Zhu, Chen-Zhuo; Bai, Zhuangfei; Miao, Qiguang; Ma, Wenping; Gong, Maoguo

doi:10.1007/s11633-020-1248-x

Cited by 27 publications

(7 citation statements)

References 119 publications

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“…e significance of online learning is not only to create a learning method across time and space but also to enable more high-quality educational resources to be shared by the majority of learners through the Internet and to provide learners with personalized teaching services. Today, online learning has become one of the most important ways of learning and online learning status is one of the important factors affecting learners' online learning performance, and it is also a problem that education researchers cannot ignore [8]. rough the use of analysis and evaluation technology, it is of great significance to objectively evaluate the learning status of online learners for improving teaching quality and learning efficiency.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Students’ Learning Status in the Foreign Language Classroom Based on Machine Vision

Yao¹,

Qin²

2022

Journal of Robotics

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In order to improve the effectiveness of the evaluation of student’s learning status in foreign language classrooms, this paper applies machine vision to classroom teaching. Through an in-depth analysis of the relative motion relationship between the end marker points of classroom feature recognition and the center point of the machine vision system window, this paper first proposes an autonomous tracking motion algorithm of the machine vision system window based on the preset field of view parameters. Moreover, this paper realizes the motion function of the window to track the marker points autonomously, completes the simulation analysis through two sets of planned trajectories and two sets of master hands to collect the actual trajectories, and verifies the correctness and feasibility of the algorithm. The research study shows that the algorithm based on machine vision proposed in this paper can effectively judge the real-time state of students in the foreign language classroom.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Students’ Learning Status in the Foreign Language Classroom Based on Machine Vision

Yao¹,

Qin²

2022

Journal of Robotics

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“…Existing surveys on remote sensing image registration mostly include single-modal general registration frameworks and matching methods, while MMRS image registration, as a very important branch, only occupies a small part of the article [1,[6][7][8]. A large part of the multi-modal image processing work is focused on medical image registration [9][10][11], and few papers specifically review the part of multi-modal images in remote sensing image processing.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

et al. 2021

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With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.

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“…Due to its strong ability for deep feature acquisition and nonlinear expression, deep learning technology is applied to image information representation or similarity measurement, as well as parameter regression of image-pairs transformation. The relevant literature review [11,12] provides a detailed review of learning-based methods in image registration from feature detection, feature matching and other related algorithms and applications [13][14][15].…”

Section: B Image Feature Extraction Based On Deep Learningmentioning

confidence: 99%

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

Wang

Hao

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate registration between SAR images is the basis for high-precision geometric correction of SAR images. The feature points extracted by conventional feature extraction methods is unsatisfactory, which is affected by imaging geometric characteristics and speckle noise of SAR images. This paper innovatively proposes a spaceborne SAR image feature learning framework to realize automatic sample generation and model training. It mainly includes two modules: The feature sample generation module based on the initial geometric information of spaceborne SAR. The initial RPC parameters of the spaceborne SAR are adopted to realize the initial positioning of SAR image, and a variety of feature extraction operators are used to match the overlapping areas to obtain high-precision matching points, which are employed as training samples for image-pairs; Pseudo-Siamese feature learning network SARPointNet for SAR image feature learning. The pseudo-siamese network is used to extract the feature points and descriptors of the sample image-pairs. The feature optimization process is realized through the descriptor constraints between image pairs, which promotes the network to improve the accuracy of feature extraction. The proposed method has been tested in mountain, hilly, flatland and urban scenarios, respectively. The results demonstrate that the correspondence points extracted by SARPointNet are evenly distributed, large quantities (at least 10 times that of other methods) and achieve high precision (The root mean square error is less than 1 pixels), which shows great advantages over other methods.

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Computational Intelligence in Remote Sensing Image Registration: A survey

Cited by 27 publications

References 119 publications

Evaluation of the Students’ Learning Status in the Foreign Language Classroom Based on Machine Vision

Evaluation of the Students’ Learning Status in the Foreign Language Classroom Based on Machine Vision

Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

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