Image Fusion of SAR and Optical Images for Identifying Antarctic Ice Features

Shah, Esha; James, M. E.

doi:10.1007/s12524-019-01040-3

Cited by 22 publications

(7 citation statements)

References 19 publications

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“…Increase in the entropy value after fusion indicates that increase in spatial information of the fused image compared to the reference image. 24 If the difference in entropy is small, it indicates a minor loss of information in the fused image and can be utilized to measure the amount of information gained or lost during the fusion process. iii.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…5,18,19 Various algorithms have been developed over the years for image fusion, such as principal component analysis (PCA), Brovey transformation (BT), intensity-hue-saturation (IHS), and Gram-Schmidt (GS) transformations. [19][20][21][22][23][24] The PCA method preserves the spectral information by enhancing the image quality and also reduces the redundancy. 8,17,25,26 Various classification algorithms, such as support vector machine, maximum likelihood, and random forest (RF) algorithms, are suggested by many researchers to perform the classification of images.…”

Section: Introductionmentioning

confidence: 99%

“…Various algorithms have been developed over the years for image fusion, such as principal component analysis (PCA), Brovey transformation (BT), intensity–hue–saturation (IHS), and Gram–Schmidt (GS) transformations 19 – 24 The PCA method preserves the spectral information by enhancing the image quality and also reduces the redundancy 8 , 17 , 25 , 26 …”

Section: Introductionmentioning

confidence: 99%

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Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification

Allu,

Mesapam

2024

J. Appl. Rem. Sens.

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Crop classification is necessary to extract information about the crop, such as its type, the area in crop that may be used to estimate health, and the yield of the crop. Remote sensing is a technique used to extract earth surface information from satellite and aerial imagery. However, to extract crop parameters accurately requires high spatial and spectral information of the data, which can be achieved through image fusion. We focus on crop classification by selecting the suitable band combination of satellite imagery and unmanned aerial vehicle (UAV) data through image fusion technique. Various fusion techniques are available to produce high spatial and spectral resolution data. Principal component analysis PAN sharpening method is used to fuse Sentinel-2A and UAV data for crop classification, and the best combinations of different bands are assessed based on the image quality metrics and classification accuracy. Random forest classification algorithm is performed to classify the fused images and the classification accuracy is assessed by using the F -score, Kappa coefficient, and overall accuracy. The best accuracy of 93.14% and a kappa value of 0.87 is achieved using random forest classification technique using fused images of red band of UAV imagery with BGRNIR (blue, green, red, and NIR) bands of Sentinel-2A imagery is high compared to other band combinations.

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Section: Quality Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification

Allu,

Mesapam

2024

J. Appl. Rem. Sens.

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“…With the continuous exploration and development of SAR imaging technology, the demand for SAR image data is getting higher and higher. However, on the other hand, SAR images are quite different from optical images and have the characteristics of correlated noise [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Process Classification and Target Recognition Algorithm for SAR Images

Yang

Zhang

2022

Scientific Programming

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Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results.

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“…Secondly, results are provided with a kilometric resolution, which is larger than the spatial resolution satellites can achieve nowadays. With the ongoing development of spatial activity and the launch of more and more Earth observation satellites, remote sensing became one common technique to monitor polar regions at high resolution (Fettweis et al, 2006(Fettweis et al, , 2011Nagler et al, 2015Nagler et al, , 2016Lievens et al, 2019;Shah et al, 2019;Nagler & Rott, 2000). The rise of active remote sensing satellites began with ERS, and more recently with the Copernicus Earth observation program from the European Union, making it possible to have near-daily radar images at a resolution of around 10 meters in open access with the Sentinel-1 constellation.…”

Section: Introductionmentioning

confidence: 99%

Comparaison Entre La Fonte De Surface Estimée Par Le Radar À Synthèse D’ouverture Sentinel-1 Et Un Modèle Climatique Régional. Cas D’étude Sur La Plateforme De Glace Du Roi Baudouin, en Antarctique De L’est

Dethinne¹,

Glaude²,

Amory³

et al. 2022

BSGLg

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Antarctica is the largest potential contributor to sea-level rise and needs to be monitored. It is also one of the first victims of global warming. However, it is often difficult to obtain high-resolution data on this vast and distant continent. Thanks to the Copernicus space program providing free and open access to high-quality data, this paper aims to show the complementarity between Sentinel-1 images and Modèle Atmosphérique régional (MAR) data over Antarctica. This study is conducted over Roi Baudouin Ice Shelf. The complementarity between the two datasets is established by a quantitative, temporal, and spatial comparison of the amplitude information of the radar signal and several variables modelled by MAR. Comparisons show strong spatial correlations between MAR variables representing melt and the backscatter coefficient recorded by the satellite. While temporal and quantitative analyses also give impressive results, further investigations are required to explain contrasting behaviors in other different areas of the ice shelf.

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Image Fusion of SAR and Optical Images for Identifying Antarctic Ice Features

Cited by 22 publications

References 19 publications

Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification

Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification

A Gaussian Process Classification and Target Recognition Algorithm for SAR Images

Comparaison Entre La Fonte De Surface Estimée Par Le Radar À Synthèse D’ouverture Sentinel-1 Et Un Modèle Climatique Régional. Cas D’étude Sur La Plateforme De Glace Du Roi Baudouin, en Antarctique De L’est

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