Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching

Ma, Wenping; Wen, Zelian; Wu, Yue; Jiao, Licheng; Gong, Maoguo; Zheng, Yanfeng; Liu, Liang

doi:10.1109/lgrs.2016.2600858

Cited by 312 publications

(172 citation statements)

References 16 publications

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“…Then evaluation criterion which consists of correct matches number (N) (Li et al, 2015;Ma et al, 2017) and root mean square error (Gong et al, 2014;Kupfer et al, 2015;Ma et al, 2017), is used to verify the accuracy and robustness of the proposed method. In the paper, we selected the two images with different perspectives that both images as shown Fig.…”

Section: Resultsmentioning

confidence: 99%

An Improved Image Matching Method Based on Surf Algorithm

Chen

Zheng

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Many state-of-the-art image matching methods, based on the feature matching, have been widely studied in the remote sensing field. These methods of feature matching which get highly operating efficiency, have a disadvantage of low accuracy and robustness. This paper proposes an improved image matching method which based on the SURF algorithm. The proposed method introduces color invariant transformation, information entropy theory and a series of constraint conditions to increase feature points detection and matching accuracy. First, the model of color invariant transformation is introduced for two matching images aiming at obtaining more color information during the matching process and information entropy theory is used to obtain the most information of two matching images. Then SURF algorithm is applied to detect and describe points from the images. Finally, constraint conditions which including Delaunay triangulation construction, similarity function and projective invariant are employed to eliminate the mismatches so as to improve matching precision. The proposed method has been validated on the remote sensing images and the result benefits from its high precision and robustness.

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Section: Resultsmentioning

confidence: 99%

An Improved Image Matching Method Based on Surf Algorithm

Chen

Zheng

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In order to evaluate the performance of the applied matching strategy in the proposed method, we compare it with the NNDR method, the fast sample consensus (FSC) method [34] and the enhanced matching method (EM) [35]. The interest point detector and descriptor described in Sections 2.1 and 2.2 are adopted to extract features for all four matching methods.…”

Section: Matching Strategies Comparisonmentioning

confidence: 99%

Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint

Chen

Habib

et al. 2017

Remote Sensing

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Improving the matching reliability of multi-sensor imagery is one of the most challenging issues in recent years, particularly for synthetic aperture radar (SAR) and optical images. It is difficult to deal with the noise influence, geometric distortions, and nonlinear radiometric difference between SAR and optical images. In this paper, a method for SAR and optical images matching is proposed. First, interest points that are robust to speckle noise in SAR images are detected by improving the original phase-congruency-based detector. Second, feature descriptors are constructed for all interest points by combining a new Gaussian-Gamma-shaped bi-windows-based gradient operator and the histogram of oriented gradient pattern. Third, descriptor similarity and geometrical relationship are combined to constrain the matching processing. Finally, an approach based on global and local constraints is proposed to eliminate outliers. In the experiments, SAR images including COSMO-Skymed, RADARSAT-2, TerraSAR-X and HJ-1C images, and optical images including ZY-3 and Google Earth images are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method provides significant improvements in the number of correct matches and matching precision compared with the state-of-the-art SIFT-like methods. Near 1 pixel registration accuracy is obtained based on the matching results of the proposed method.

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“…According to the taxonomy given in [5] data fusion methods, i.e., processing dealing with data and information from multiple sources to achieve improved information for decision making can be grouped into three main categories: -pixel-level: the pixel values of the sources to be fused are jointly processed [6][7][8][9]; -feature-level: features like lines, regions, keypoints, maps, and so on, are first extracted independently from each source image and subsequently combined to produce higher-level cross-source features, which may represent the desired output or be further processed [10][11][12][13][14][15][16][17]; -decision-level: the high-level information extracted independently from each source is combined to provide the final outcome, for example using fuzzy logic [18,19], decision trees [20], Bayesian inference [21], Dempster-Shafer theory [22], and so forth.…”

Section: Introductionmentioning

confidence: 99%

“…It represents also the most challenging case because of the several sources of mismatch (temporal, geometrical, spectral, radiometric) among the involved data. As for other categories, a number of typical remote sensing problems can fit this paradigm, such as classification [10,16,[35][36][37], coregistration [15], change detection [38] and feature estimation [4,[39][40][41]. -mixed: the above cases may also occur jointly, generating mixed situations.…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

et al. 2018

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Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR) images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account-optical sequences, SAR sequences, digital elevation model-so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN) is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI), using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May-November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators.

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Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching

Cited by 312 publications

References 16 publications

An Improved Image Matching Method Based on Surf Algorithm

An Improved Image Matching Method Based on Surf Algorithm

Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

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