Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

Zhang, Xinyue; Leng, Chengcai; Hong, Yameng; Pei, Zhao; Cheng, Irene; Basu, Anup

doi:10.3390/rs13245128

Cited by 40 publications

(27 citation statements)

References 107 publications

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“…Geometric registration may be performed using RPC, SIFT, DTM, CACO, and RANSAC [103,104]. Aside from these methods, a registration strategy known as Harris-Laplace is used for CD [105,106]. To improve the accuracy of point detection, the identified points are grouped and balanced using SIFT in this method.…”

Section: Geometric Cegistrationmentioning

confidence: 99%

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

et al. 2022

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Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods.

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Section: Geometric Cegistrationmentioning

confidence: 99%

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

et al. 2022

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“…In the second direction, applications of the "deep learning algorithm" in remote sensing image registration are presented. X. Zhang et al [17] presented a traditional methods of image registration and their main focus is an advanced methods for "multimodal remote sensing (MMRS)" image registration algorithms. An algorithm called enhanced affline transformation (EAT) is presented for "non-rigid infrared (IR) and visible (VIS)" image registration [18].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid approach for Image Acquisition Methods based on Feature Based Image Registration (FBIR)

Kumawat

Panda

2022

Preprint

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In this paper, the problem of Feature-Based Image Registration (FBIR) has been addressed using a novel hybrid feature detector. To prove the efficiency of the proposed hybrid feature detector, its performance is compared with that of the state-of-the-art feature detectors like BRISK, FAST, ORB, Harris, MinEigen and MSER. Three different image acquisition methods are choosen for comparison. They are (i) Rotation transformation (ii) Scene to model transformation and (iii) Scaling transformation. Taking into consideration the above three types of image acquisition methods, a FBIR method is proposed for different types of remote sensing images. This paper proposed a hybrid feature detection algorithm, that consumes less time to detect the feature key points and it is also rotation and scale invariant. In addition to this the proposed algorithm performs well in terms of match points and match rate. This paper also focuses on a comparative analysis of BRISK, FAST, Harris, MinEigen, MSER and ORB with the proposed algorithm in terms of time to detect feature keypoints. It can be observed from the results and tables that in the case of hybrid feature detectors,It takes less time to detect the feature key points.

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“…Generally speaking, traditional registration methods can be divided into two types, area-based methods and feature-based methods [10][11][12]. Area-based methods, using a template matching scheme, adopt the original pixel values of images to establish the similarity metric between image pairs, so as to determine the corresponding points.…”

Section: Introductionmentioning

confidence: 99%

A TIR-Visible Automatic Registration and Geometric Correction Method for SDGSAT-1 Thermal Infrared Image Based on Modified RIFT

et al. 2022

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High-resolution thermal infrared (TIR) remote sensing images can more accurately retrieve land surface temperature and describe the spatial pattern of urban thermal environment. The Thermal Infrared Spectrometer (TIS), which has high spatial resolution among spaceborne thermal infrared sensors at present, and global data acquisition capability, is one of the sensors equipped in the SDGSAT-1. It is an important complement to the existing international mainstream satellites. In order to produce standard data products, rapidly and accurately, the automatic registration and geometric correction method needs to be developed. Unlike visible–visible image registration, thermal infrared images are blurred in edge details and have obvious non-linear radiometric differences from visible images, which make it challenging for the TIR-visible image registration task. To address these problems, homomorphic filtering is employed to enhance TIR image details and the modified RIFT algorithm is proposed to achieve TIR-visible image registration. Different from using MIM for feature description in RIFT, the proposed modified RIFT uses the novel binary pattern string to descriptor construction. With sufficient and uniformly distributed ground control points, the two-step orthorectification framework, from SDGSAT-1 TIS L1A image to L4 orthoimage, are proposed in this study. The first experiment, with six TIR-visible image pairs, captured in different landforms, is performed to verify the registration performance, and the result indicates that the homomorphic filtering and modified RIFT greatly increase the number of corresponding points. The second experiment, with one scene of an SDGSAT-1 TIS image, is executed to test the proposed orthorectification framework. Subsequently, 52 GCPs are selected manually to evaluate the orthorectification accuracy. The result indicates that the proposed orthorectification framework is helpful to improve the geometric accuracy and guarantee for the subsequent thermal infrared applications.

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Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

Cited by 40 publications

References 107 publications

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Deep Learning-Based Change Detection in Remote Sensing Images: A Review

A Hybrid approach for Image Acquisition Methods based on Feature Based Image Registration (FBIR)

A TIR-Visible Automatic Registration and Geometric Correction Method for SDGSAT-1 Thermal Infrared Image Based on Modified RIFT

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