Novel Techniques for Built-Up Area Extraction from Polarimetric SAR Images

10.26686/wgtn.12554141 ◽

2020 ◽

Author(s):

D Ratha ◽

P Gamba ◽

A Bhattacharya ◽

Alejandro Frery

Keyword(s):

Remote Sensing ◽

Unit Sphere ◽

Geodesic Distance ◽

Synthetic Aperture ◽

Extraction Techniques ◽

Remote Sensing Images ◽

Sar Images ◽

Polarimetric Synthetic Aperture Radar ◽

Urban Scenes ◽

L Band

© 2004-2012 IEEE. Built-up (BU) area extraction from remote sensing images is important to monitor and manage urbanization and industrialization. In this letter, we propose two BU area extraction techniques based on the analysis of fully polarimetric synthetic aperture radar (PolSAR) data. Both methods exploit the geodesic distance on the unit sphere in the space of Kennaugh matrices. The first method is based on the three dominant scattering types in the scene and compares them with scattering models; if any of them matches with BU type elementary scattering models, then the pixel is said to belong to a BU area. The second method is based on a novel PolSAR BU index (RBUI) composed by considering scattering mechanisms from BU structures. The two proposed techniques are validated on two different urban scenes, one acquired at C-band by RADARSAT-2 and other at L-band by ALOS-2 SAR sensors.

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Application of the method of renormalization with limitation to remote sensing images obtained with synthetic aperture radars.

Journal of Radio Electronics ◽

10.30898/1684-1719.2021.3.4 ◽

2021 ◽

Vol 2021 (3) ◽

Author(s):

A.V. Kokoshkin ◽

Keyword(s):

Remote Sensing ◽

Speckle Noise ◽

Synthetic Aperture ◽

Reference Spectrum ◽

Sar Image ◽

Remote Sensing Images ◽

Sar Images ◽

Synthetic Aperture Radars ◽

Universal Reference ◽

Spectrum Model

This article proposes an application of the method of renormalization with limitation (MRL) to suppress speckle noise in SAR images. This is because the method of renormalization with limitation, by its definition, renormalizes the SAR image spectrum to a universal reference spectrum (URS) model, which is a "good" quality grayscale spectrum model. To increase the overall sharpness of the image, consistently with the MRL, it is proposed to apply the classical Laplacian. This study allows us to conclude that the application of MRL to SAR images can significantly reduce speckle noise.

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Coupling Denoising to Detection for SAR Imagery

Applied Sciences ◽

10.3390/app11125569 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5569

Author(s):

Sujin Shin ◽

Youngjung Kim ◽

Insu Hwang ◽

Junhee Kim ◽

Sungho Kim

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Performance Improvement ◽

Information Loss ◽

Synthetic Aperture ◽

Sar Image ◽

Remote Sensing Images ◽

Sar Images ◽

Backbone Networks ◽

Sar Imagery

Detecting objects in synthetic aperture radar (SAR) imagery has received much attention in recent years since SAR can operate in all-weather and day-and-night conditions. Due to the prosperity and development of convolutional neural networks (CNNs), many previous methodologies have been proposed for SAR object detection. In spite of the advance, existing detection networks still have limitations in boosting detection performance because of inherently noisy characteristics in SAR imagery; hence, separate preprocessing step such as denoising (despeckling) is required before utilizing the SAR images for deep learning. However, inappropriate denoising techniques might cause detailed information loss and even proper denoising methods does not always guarantee performance improvement. In this paper, we therefore propose a novel object detection framework that combines unsupervised denoising network into traditional two-stage detection network and leverages a strategy for fusing region proposals extracted from both raw SAR image and synthetically denoised SAR image. Extensive experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from TerraSAR-X and COSMO-SkyMed satellites. Extensive experiments validate the effectiveness of our framework on our own object detection datasets constructed with remote sensing images from TerraSAR-X and COSMO-SkyMed satellites. The proposed framework shows better performances when we compared the model with using only noisy SAR images and only denoised SAR images after despeckling under multiple backbone networks.

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FULL POLARIMETRIC UAVSAR IMAGE CHANGE DETECTION BASED ON CHANGE INDICES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-1107-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 1107-1111

Author(s):

L. Yousefizadeh ◽

R. Shahhoseini ◽

S. Homayouni

Keyword(s):

Change Detection ◽

Detection Method ◽

Synthetic Aperture ◽

Sar Images ◽

Polarimetric Synthetic Aperture Radar ◽

San Andreas ◽

L Band ◽

Image Change Detection ◽

Dimension Space ◽

Change Detection Performance

Abstract. Change detection is one of the most important applications of Polarimetric Synthetic Aperture Radar (PolSAR) data in monitoring urban development and supporting urban planning due to the sensibility of SAR signal to geometrical and physical properties of terrestrial features. In this paper, we proposed an unsupervised change detection method using change indices extracted from PolSAR images. Kernel k-means clustering was then performed to extract changed areas. The kernel k-means clustering is an unsupervised algorithm that maps the input features to higher Hilbert dimension space by using a kernel function. To better representation of changed areas, different change indices were generated. The method was applied to UAVSAR L-band SAR images acquired over an urban area in San Andreas, United States. We evaluated the change detection performance based on kappa and overall accuracies of the proposed approach and compared with other well-known classic methods.

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TRANSFORMATION BASED ALGORITHMS FOR CHANGE DETECTION IN FULL POLARIMETRIC REMOTE SENSING IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-963-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 963-967 ◽

Cited By ~ 1

Author(s):

S. T. Seydi ◽

R. Shahhoseini

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Electromagnetic Waves ◽

Visual Analysis ◽

Principal Component ◽

Synthetic Aperture ◽

Remote Sensing Images ◽

Polarimetric Synthetic Aperture Radar ◽

Common Criteria ◽

Cross Covariance

Abstract. Thanks to the recent advances in the development of polarimetric synthetic aperture radar (SAR) sensors, this remote sensing field attracts many applications. Among the different applications of these data, change detection is one of the most important applications. PolSAR images, due to interactions between electromagnetic waves and the target, could be used to study changes in the Earth's surface. This paper is a type of transformation-based method for polarimetric change detection (CD) purpose. For this purpose, we use full polarimetry imaging radar and extracted 138 features based on decomposition. The CD methods are the principal component analysis (PCA), the Multivariate Alteration Detection (MAD), the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD), the Covariance Equalization (CE), and the Cross-Covariance (CRC). Assessment of the incorporated methods performed using most common criteria as quantity and quality assessment, such as overall accuracy (OA), kappa coefficient, and as visual analysis. The results of the experiments show that CC has better performance compared with other algorithms.

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A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems

Sensors ◽

10.3390/s18103580 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3580 ◽

Cited By ~ 1

Author(s):

Jie Wang ◽

Ke-Hong Zhu ◽

Li-Na Wang ◽

Xing-Dong Liang ◽

Long-Yong Chen

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Synthetic Aperture Radar ◽

3D Imaging ◽

Synthetic Aperture ◽

Separation Scheme ◽

Sar Images ◽

Wide Swath ◽

Aperture Radar

In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.

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Building Extraction from High-Resolution Remote Sensing Images Based on GrabCut with Automatic Selection of Foreground and Background Samples

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.86.4.235 ◽

2020 ◽

Vol 86 (4) ◽

pp. 235-245 ◽

Cited By ~ 1

Author(s):

Ka Zhang ◽

Hui Chen ◽

Wen Xiao ◽

Yehua Sheng ◽

Dong Su ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Geodesic Distance ◽

Extraction Methods ◽

Building Extraction ◽

Remote Sensing Images ◽

Canny Operator ◽

Contour Lines ◽

Average Accuracy ◽

Seed Points

This article proposes a new building extraction method from high-resolution remote sensing images, based on GrabCut, which can automatically select foreground and background samples under the constraints of building elevation contour lines. First the image is rotated according to the direction of pixel displacement calculated by the rational function Model. Second, the Canny operator, combined with morphology and the Hough transform, is used to extract the building's elevation contour lines. Third, seed points and interesting points of the building are selected under the constraint of the contour line and the geodesic distance. Then foreground and background samples are obtained according to these points. Fourth, GrabCut and geometric features are used to carry out image segmentation and extract buildings. Finally, WorldView satellite images are used to verify the proposed method. Experimental results show that the average accuracy can reach 86.34%, which is 15.12% higher than other building extraction methods.

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ANALYSIS OF SCATTERING COMPONENTS FROM FULLY POLARIMETRIC SAR IMAGES FOR IMPROVING ACCURACIES OF URBAN DENSITY ESTIMATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-7-219-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 219-226

Author(s):

J. Susaki

Keyword(s):

Normal Distribution ◽

Urban Areas ◽

Synthetic Aperture ◽

Sar Image ◽

Polarimetric Sar ◽

Sar Images ◽

Polarimetric Synthetic Aperture Radar ◽

Urban Density ◽

Concrete Blocks ◽

Image Pixels

In this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index Tv+c obtained by normalizing the sum of volume and helix scatterings Pv+c. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why Tv+c is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of Pv+c are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that Tv+c works most effectively because of its similarity to the normal distribution.

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SAR-Based Flood Monitoring for Flatland with Frequently Fluctuating Water Surfaces: Proposal for the Normalized Backscatter Amplitude Difference Index (NoBADI)

Remote Sensing ◽

10.3390/rs13204136 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4136

Author(s):

Hiroto Nagai ◽

Takahiro Abe ◽

Masato Ohki

Keyword(s):

Water Surface ◽

Synthetic Aperture ◽

Sar Image ◽

Sar Images ◽

Simple Method ◽

Multiple Images ◽

Amplitude Difference ◽

The Us ◽

Water Surfaces ◽

L Band

Space-based synthetic aperture radar (SAR) is a powerful tool for monitoring flood conditions over large areas without the influence of clouds and daylight. Permanent water surfaces can be excluded by comparing SAR images with pre-flood images, but fluctuating water surfaces, such as those found in flat wetlands, introduce uncertainty into flood mapping results. In order to reduce this uncertainty, a simple method called Normalized Backscatter Amplitude Difference Index (NoBADI) is proposed in this study. The NoBADI is calculated from a post-flood SAR image of backscatter amplitude and multiple images on non-flooding conditions. Preliminary analysis conducted in the US state of Florida, which was affected by Hurricane Irma in September 2017, shows that surfaces frequently covered by water (more than 20% of available data) have been successfully excluded by means of C-/L-band SAR (HH, HV, VV, and VH polarizations). Although a simple comparison of pre-flood and post-flood images is greatly affected by the spatial distribution of the water surface in the pre-flood image, the NoBADI method reduces the uncertainty of the reference water surface. This advantage will contribute in making quicker decisions during crisis management.

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