scholarly journals Improving Co-Registration for Sentinel-1 SAR and Sentinel-2 Optical Images

2021 ◽  
Vol 13 (5) ◽  
pp. 928
Author(s):  
Yuanxin Ye ◽  
Chao Yang ◽  
Bai Zhu ◽  
Liang Zhou ◽  
Youquan He ◽  
...  
Keyword(s):  
European Space Agency ◽  
Phase Correlation ◽  
Optical Data ◽  
Geometric Transformation ◽  
Interest Points ◽  
Registration Accuracy ◽  
Registration Method ◽  
Sensing Applications ◽  
Optical Images ◽  
Sentinel 2

Co-registering the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data of the European Space Agency (ESA) is of great importance for many remote sensing applications. However, we find that there are evident misregistration shifts between the Sentinel-1 SAR and Sentinel-2 optical images that are directly downloaded from the official website. To address that, this paper presents a fast and effective registration method for the two types of images. In the proposed method, a block-based scheme is first designed to extract evenly distributed interest points. Then, the correspondences are detected by using the similarity of structural features between the SAR and optical images, where the three-dimensional (3D) phase correlation (PC) is used as the similarity measure for accelerating image matching. Lastly, the obtained correspondences are employed to measure the misregistration shifts between the images. Moreover, to eliminate the misregistration, we use some representative geometric transformation models such as polynomial models, projective models, and rational function models for the co-registration of the two types of images, and we compare and analyze their registration accuracy under different numbers of control points and different terrains. Six pairs of the Sentinel-1 SAR L1 and Sentinel-2 optical L1C images covering three different terrains are tested in our experiments. Experimental results show that the proposed method can achieve precise correspondences between the images, and the third-order polynomial achieves the most satisfactory registration results. Its registration accuracy of the flat areas is less than 1.0 10 m pixel, that of the hilly areas is about 1.5 10 m pixels, and that of the mountainous areas is between 1.7 and 2.3 10 m pixels, which significantly improves the co-registration accuracy of the Sentinel-1 SAR and Sentinel-2 optical images.

scholarly journals FAST AND ROBUST REGISTRATION OF AERIAL IMAGES AND LIDAR DATA BASED ON STRUCTURAL FEATURES AND 3D PHASE CORRELATION

2020 ◽  
Vol V-2-2020 ◽  
pp. 135-141
Author(s):  
B. Zhu ◽  
Y. Ye ◽  
C. Yang ◽  
L. Zhou ◽  
H. Liu ◽  
...  
Keyword(s):  
Structural Features ◽  
Phase Correlation ◽  
Aerial Images ◽  
Aerial Image ◽  
Reference Image ◽  
Feature Descriptor ◽  
Lidar Data ◽  
Three Dimension ◽  
Interest Points ◽  
Registration Method

Abstract. Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quilt challenging because the different imaging mechanism causes significant geometric and radiometric distortions between such data. To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation. In the proposed method, the LiDAR point cloud data is first transformed into the intensity map, which is used as the reference image. Then, we employ the Fast operator to extract uniformly distributed interest points in the aerial image by a partition strategy and perform a local geometric correction by using the collinearity equation to eliminate scale and rotation difference between images. Subsequently, a robust structural feature descriptor is build based on dense gradient features, and the 3D phase correlation is used to detect control points (CPs) between aerial images and LiDAR data in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT). Finally, the obtained CPs are employed to correct the exterior orientation elements, which is used to achieve co-registration of aerial images and LiDAR data. Experiments with two datasets of aerial images and LiDAR data show that the proposed method is much faster and more robust than state of the art methods.

scholarly journals A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences

2019 ◽  
Vol 11 (4) ◽  
pp. 470 ◽  
Author(s):  
Kai Li ◽  
Yongsheng Zhang ◽  
Zhenchao Zhang ◽  
Guangling Lai
Keyword(s):  
Reference Image ◽  
Registration Accuracy ◽  
Registration Method ◽  
Normalized Mutual Information ◽  
Sensing Applications ◽  
Validation Experiment ◽  
Image Pairs ◽  
Coarse To Fine ◽  
Fine Registration ◽  
Three Phases

Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times.

scholarly journals ROBUST REGISTRATION FOR OPTICAL AND SAR IMAGES BASED ON SPATIAL GEOMETRIC CONSTRAINT AND STRUCTURE FEATURES

2020 ◽  
Vol V-1-2020 ◽  
pp. 117-124
Author(s):  
M. Wang ◽  
Y. Ye ◽  
M. Sun ◽  
X. Tan ◽  
L. Li
Keyword(s):  
Template Matching ◽  
Three Dimensional ◽  
Phase Correlation ◽  
Geometric Constraint ◽  
Feature Descriptor ◽  
Automatic Registration ◽  
Geometric Correction ◽  
Registration Accuracy ◽  
Sar Images ◽  
Registration Method

Abstract. Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to significant geometric deformation and radiometric differences between two images. To address this issue, this paper proposes an automatic registration method for optical and SAR images based on spatial geometric constraint and structure features. Firstly, the Harris detector with a block strategy is used to extract evenly distributed feature points in the images. Subsequently, a local geometric correction is performed by using the Rational Function Model, which eliminates the rotation and scale differences between optical and SAR images. Secondly, orientated gradient information of images is used to construct a geometric structural feature descriptor. Then, the feature descriptor is transformed into the frequency domain, and the three-dimensional (3-D) phase correlation is used as the similarity metric to achieve correspondences by employing a template matching scheme. Finally, mismatches are eliminated based on spatial geometric constraint relationship between images, followed by a process of geometric correction to achieve the image registration. Experimental results with multiple high-resolution optical and SAR images show that the proposed method can achieve reliable registration accuracy, and outperforms the state of the art methods.

scholarly journals KappaMask: AI-Based Cloudmask Processor for Sentinel-2

2021 ◽  
Vol 13 (20) ◽  
pp. 4100
Author(s):  
Marharyta Domnich ◽  
Indrek Sünter ◽  
Heido Trofimov ◽  
Olga Wold ◽  
Fariha Harun ◽  
...  
Keyword(s):  
Land Surface ◽  
European Space Agency ◽  
Optical Data ◽  
Dice Coefficient ◽  
Test Dataset ◽  
Space Agency ◽  
Northern Latitudes ◽  
Agricultural Applications ◽  
Cloud Mask ◽  
Sentinel 2

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.

scholarly journals Oil Spill Index (OSI) to Sentinel-2 Satellite Data: QU in International Contribution

2021 ◽  
Author(s):  
S Rajendran ◽  
AS Fahad ◽  
FN Sadooni ◽  
HAS Al-Kuwari ◽  
P Vethamony ◽  
...  
Keyword(s):  
Oil Spill ◽  
Satellite Data ◽  
Emergency Preparedness ◽  
Oil Spills ◽  
European Space Agency ◽  
Optical Data ◽  
Spectral Absorption ◽  
Spectral Bands ◽  
Space Agency ◽  
Sentinel 2

An Oil Spill Index (OSI = (B3+B4)/B2) was developed and applied to Sentinel-2 optical satellite data of the European Space Agency (ESA) to map marine oil spills using spectral absorption characters of spectral bands of the Sentinel-2. The potential application of OSI and derived indices [i. (5+6)/7, (3+4)/2, (11+12)/8 and ii. 3/2, (3+4)/2, (6+7)/5] were demonstrated to the oil spills that occurred off Mauritius, Indian Ocean, on August 06, 2020, and Norilsk region, Russia on May 29, 2020, and the results were published in the peer-reviewed research journals. Recently (August 19, 2021), our methodology was recognized by the Sentinel-Hub (a repository of custom scripts) https://custom-scripts.sentinel-hub.com/sentinel-2/oil-spill-index/ for OSI calculation. We validated the remote sensing results with the drone images taken during the incident. Our OSI index is the first to be applied to Sentinel-2 optical data to map oil spills. We proved the potential of indices and the capability of Sentinel sensors to detect, map, monitor, and assess the oil spill, which can be used for emergency preparedness of oil spills.

scholarly journals Detection of Seasonal Inundations by Satellite Data at Shkoder Urban Area, North Albania for Sustainable Management

2019 ◽  
Vol 11 (16) ◽  
pp. 4454 ◽  
Author(s):  
Stefano Morelli ◽  
Matteo Del Soldato ◽  
Silvia Bianchini ◽  
Veronica Pazzi ◽  
Ervis Krymbi ◽  
...  
Keyword(s):  
European Space Agency ◽  
Temporal Analysis ◽  
Radar Data ◽  
Optical Data ◽  
Future Perspective ◽  
Management Service ◽  
Surface Mapping ◽  
Water Index ◽  
Space Agency ◽  
Sentinel 2

The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkodër area, which occurred in the two last years (2017–2018). For instance, in March 2018 the Shkodër district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkodër lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkodër city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives.

scholarly journals Optimal parameter selection for intensity-based multi-sensor data registration

2014 ◽  
Vol II-3 ◽  
pp. 95-102 ◽  
Author(s):  
E. G. Parmehr ◽  
C. S. Fraser ◽  
C. Zhang ◽  
J. Leach
Keyword(s):  
Remote Sensing ◽  
Optimal Parameter ◽  
Parameter Selection ◽  
Sensor Data ◽  
Geometric Transformation ◽  
Registration Method ◽  
Sensing Applications ◽  
Joint Histogram ◽  
Pdf Estimation ◽  
Parameter Values

Accurate co-registration of multi-sensor data is a primary step in data integration for photogrammetric and remote sensing applications. A proven intensity-based registration approach is Mutual Information (MI). However the effectiveness of MI for automated registration of multi-sensor remote sensing data can be impacted to the point of failure by its non-monotonic convergence surface. Since MI-based methods rely on joint probability density functions (PDF) for the datasets, errors in PDF estimation can directly affect the MI value. Certain PDF parameter values, such as the bin-size of the joint histogram and the smoothing kernel, need to be assigned in advance, since they play a key role in forming the convergence surface. The lack of a general approach to the assignment of these parameter values for various data types reduces both the automation level and the robustness of registration. This paper proposes a new approach for selection of optimal parameter values for PDF estimation in MI-based registration of optical imagery to LiDAR point clouds. The proposed method determines the best parameters for PDF estimation via an analysis of the relationship between similarity measure values of the data and the adopted geometric transformation in order to achieve the optimal registration reliability. The performance of the proposed parameter selection method is experimentally evaluated and the obtained results are compared with those achieved through a feature-based registration method.

scholarly journals A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6298
Author(s):  
Jinjun Meng ◽  
Jiaqi Wu ◽  
Linlin Lu ◽  
Qingting Li ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Delaunay Triangulation ◽  
Affine Transformation ◽  
Hyperspectral Imagery ◽  
Geometric Transformation ◽  
Registration Accuracy ◽  
Full Spectrum ◽  
Differential Correction ◽  
Registration Method ◽  
Transformation Relation

Accurate registration is an essential prerequisite for analysis and applications involving remote sensing imagery. It is usually difficult to extract enough matching points for inter-band registration in hyperspectral imagery due to the different spectral responses for land features in different image bands. This is especially true for non-adjacent bands. The inconsistency in geometric distortion caused by topographic relief also makes it inappropriate to use a single affine transformation relationship for the geometric transformation of the entire image. Currently, accurate registration between spectral bands of Zhuhai-1 satellite hyperspectral imagery remains challenging. In this paper, a full-spectrum registration method was proposed to address this problem. The method combines the transfer strategy based on the affine transformation relationship between adjacent spectrums with the differential correction from dense Delaunay triangulation. Firstly, the scale-invariant feature transform (SIFT) extraction method was used to extract and match feature points of adjacent bands. The RANdom SAmple Consensus (RANSAC) algorithm and the least square method is then used to eliminate mismatching point pairs to obtain fine matching point pairs. Secondly, a dense Delaunay triangulation was constructed based on fine matching point pairs. The affine transformation relation for non-adjacent bands was established for each triangle using the affine transformation relation transfer strategy. Finally, the affine transformation relation was used to perform differential correction for each triangle. Three Zhuhai-1 satellite hyperspectral images covering different terrains were used as experiment data. The evaluation results showed that the adjacent band registration accuracy ranged from 0.2 to 0.6 pixels. The structural similarity measure and cosine similarity measure between non-adjacent bands were both greater than 0.80. Moreover, the full-spectrum registration accuracy was less than 1 pixel. These registration results can meet the needs of Zhuhai-1 hyperspectral imagery applications in various fields.

The Study of a Fast Sub-Pixel Registration Method for Remote Sensing Image

2014 ◽  
Vol 989-994 ◽  
pp. 3877-3880 ◽  
Author(s):  
Cui Zhou ◽  
Jing Hong Zhou ◽  
Dong Hao Fan
Keyword(s):  
Remote Sensing ◽  
Fourier Transform ◽  
Remote Sensing Image ◽  
Phase Correlation ◽  
Engineering Practice ◽  
Improved Method ◽  
Registration Accuracy ◽  
Total Size ◽  
Transform Method ◽  
Registration Method

We put forward a fast and efficiently sub-pixel registration method for solving the classical methods’ problems of low efficiency, and use efficiently sub-images instead of original image to sub-pixel registration based on the Fourier transform phase correlation and matrix Fourier transform method. Effective sub-images are selected from the total size of the high-frequency energy after two-dimensional wavelet decomposition, then we use the phase correlation to calculate the pixel displacement and matrix Fourier transform to calculate the sub-pixel displacement. Not only the improved method is inherited the advantage of matrix Fourier transform sub-pixel registration, but also the registration speed is greatly improved. This is more applicable to massive remote sensing data. Through simulation and engineering practice, composited registration accuracy and speed, proved that the improved method is more efficient compared with the classical methods, and it’s more suitable for real remote sensing image registration.

scholarly journals Aplicación de imágenes Sentinel-1 y Sentinel-2 en la detección y delineación de información de crisis de desastres naturales en el marco de los servicios Copernicus EMS

2017 ◽  
pp. 49 ◽  
Author(s):  
U. Donezar-Hoyos ◽  
A. Larrañaga Urien ◽  
A. Tamés-Noriega ◽  
C. Sánchez-Gil ◽  
L. Albizua-Huarte ◽  
...  
Keyword(s):  
European Space Agency ◽  
Earth Observation ◽  
High Spectral Resolution ◽  
Radar Images ◽  
Space Agency ◽  
Optical Images ◽  
Different Types ◽  
Burnt Areas ◽  
Flooded Areas ◽  
Sentinel 2

<p>This study shows the inclusion of Sentinel-1 and Sentinel-2 images in the workflows to obtain of crisis information of different types of events and their applicability in the detection and monitoring of those events. Sentinel is an Earth Observation (EO) program that is currently being developed by the European Space Agency (ESA) in the scope of the Copernicus program operative since April 2012, formerly known as Global Monitoring for Environment and Security (GMES). This program comprises six missions, out of which three are active, Sentinel-1 that provides radar images, Sentinel-.2 providing High Resolution optical images and Sentinel-3 developed to support GMES ocean, land, atmospheric, emergency, security and cryospheric applications. The present paper describes the use of Sentinel-1 radar to detect and delineate flooded areas, and the MultiTemporal Coherence (MTC) analysis applied with pre and post-event images to delimit and monitor burnt areas and lava flows. With respect to Sentinel-2, its high spectral resolution bands allowed the delineation of burnt areas by calculating differences of vegetation and burnt indices using pre and postevent images. Results using Sentinel-1 and Sentinel-2 data were compared with results using higher spatial resolution images, both optical and radar. In all cases, the usability of Sentinel images was proven.</p>

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