Evaluation of existing image matching methods for deriving glacier surface displacements globally from optical satellite imagery

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

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Affine versus projective transformation for SIFT and RANSAC image matching methods

2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) ◽

10.1109/icsipa.2015.7412233 ◽

2015 ◽

Cited By ~ 4

Author(s):

Redia Redzuwan ◽

N. A. M. Radzi ◽

N. M. Din ◽

I. S. Mustafa

Keyword(s):

Image Matching ◽

Projective Transformation ◽

Matching Methods

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Comparing the image matching methods of the two-stage and FFT

Close-Range Photogrammetry Meets Machine Vision ◽

10.1117/12.2294375 ◽

1990 ◽

Author(s):

Jin-Tsong Hwang

Keyword(s):

Image Matching ◽

Two Stage ◽

Matching Methods

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An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery

Remote Sensing ◽

10.3390/rs8080672 ◽

2016 ◽

Vol 8 (8) ◽

pp. 672 ◽

Cited By ~ 13

Author(s):

Xiao Ling ◽

Yongjun Zhang ◽

Jinxin Xiong ◽

Xu Huang ◽

Zhipeng Chen

Keyword(s):

Image Segmentation ◽

Satellite Imagery ◽

Image Matching ◽

Matching Algorithm

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Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization

Advances in Multimedia ◽

10.1155/2018/1504691 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15

Author(s):

Terumasa Aoki ◽

Van Nguyen

Keyword(s):

Image Matching ◽

Feature Vector ◽

Color Image ◽

Computational Cost ◽

Texture Feature ◽

Feature Descriptor ◽

Matching Methods ◽

Nonlinear Feature ◽

Efficient Matching ◽

Descriptive Feature

Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know, there are no optimal matching methods for automatic colorization because the requirements for pixel matching in automatic colorization are wholly different from those for traditional image matching. To design an efficient matching algorithm for automatic colorization, clustering pixel with low computational cost and generating descriptive feature vector are the most important challenges to be solved. In this paper, we present a novel method to address these two problems. In particular, our work concentrates on solving the second problem (designing a descriptive feature vector); namely, we will discuss how to learn a descriptive texture feature using scaled sparse texture feature combining with a nonlinear transformation to construct an optimal feature descriptor. Our experimental results show our proposed method outperforms the state-of-the-art methods in terms of robustness for color reconstruction for automatic colorization applications.

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Exploiting High Geopositioning Accuracy of SAR Data to Obtain Accurate Geometric Orientation of Optical Satellite Images

Remote Sensing ◽

10.3390/rs13173535 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3535

Author(s):

Zhongli Fan ◽

Li Zhang ◽

Yuxuan Liu ◽

Qingdong Wang ◽

Sisi Zlatanova

Keyword(s):

Satellite Imagery ◽

Image Matching ◽

Satellite Images ◽

Three Dimensional ◽

Phase Correlation ◽

Feature Descriptor ◽

Matching Method ◽

Reference Images ◽

Sar Data ◽

Optical Satellite Images

Accurate geopositioning of optical satellite imagery is a fundamental step for many photogrammetric applications. Considering the imaging principle and data processing manner, SAR satellites can achieve high geopositioning accuracy. Therefore, SAR data can be a reliable source for providing control information in the orientation of optical satellite images. This paper proposes a practical solution for an accurate orientation of optical satellite images using SAR reference images to take advantage of the merits of SAR data. Firstly, we propose an accurate and robust multimodal image matching method to match the SAR and optical satellite images. This approach includes the development of a new structural-based multimodal applicable feature descriptor that employs angle-weighted oriented gradients (AWOGs) and the utilization of a three-dimensional phase correlation similarity measure. Secondly, we put forward a general optical satellite imagery orientation framework based on multiple SAR reference images, which uses the matches of the SAR and optical satellite images as virtual control points. A large number of experiments not only demonstrate the superiority of the proposed matching method compared to the state-of-the-art methods but also prove the effectiveness of the proposed orientation framework. In particular, the matching performance is improved by about 17% compared with the latest multimodal image matching method, namely, CFOG, and the geopositioning accuracy of optical satellite images is improved, from more than 200 to around 8 m.

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