Very High-Resolution Remote Sensing: Challenges and Opportunities [Point of View]

Benediktsson, Jón Atli; Chanussot, Jocelyn; Moon, Wooil M.

doi:10.1109/jproc.2012.2190811

Cited by 93 publications

(56 citation statements)

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“…Deep learning has made significant progress in analyzing optical, LiDAR [31], and SAR [32][33][34][35][36] data. Extending these architectures (or modifying them) for SAR data processing promises even better results.…”

Section: Discussionmentioning

confidence: 99%

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

et al. 2017

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Abstract:This article reviews the use of synthetic aperture radar remote sensing data for earthen levee mapping with an emphasis on finding the slump slides on the levees. Earthen levees built on the natural levees parallel to the river channel are designed to protect large areas of populated and cultivated land in the Unites States from flooding. One of the signs of potential impending levee failure is the appearance of slump slides. On-site inspection of levees is expensive and time-consuming; therefore, a need to develop efficient techniques based on remote sensing technologies is mandatory to prevent failures under flood loading. Analysis of multi-polarized radar data is one of the viable tools for detecting the problem areas on the levees. In this study, we develop methods to detect anomalies on the levee, such as slump slides and give levee managers new tools to prioritize their tasks. This paper presents results of applying the National Aeronautics and Space Administration (NASA) Jet Propulsion Lab (JPL)'s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) quad-polarized L-band data to detect slump slides on earthen levees. The study area encompasses a portion of levees of the lower Mississippi River in the United States. In this paper, we investigate the performance of polarimetric and texture features for efficient levee classification. Texture features derived from the gray level co-occurrence (GLCM) matrix and discrete wavelet transform were computed and analyzed for efficient levee classification. The pixel-based polarimetric decomposition features, such as entropy, anisotropy, and scattering angle were also computed and applied to the support vector machine classifier to characterize the radar imagery and compared the results with texture-based classification. Our experimental results showed that inclusion of textural features derived from the SAR data using the discrete wavelet transform (DWT) features and GLCM features provided higher overall classification accuracies compared to the pixel-based polarimetric features.

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Section: Discussionmentioning

confidence: 99%

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

et al. 2017

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“…l n = P 1 (U,V,W) P 2 (U,V,W) s n = P 3 (U,V,W) P 4 (U,V,W) (1) with l n = (l − l 0 )/l S s n = (s − s 0 )/s S (2) and…”

Section: The Rational Function Modelmentioning

confidence: 99%

“…To eliminate unnecessary degrees of freedom, the zero-order terms of the denominator polynomials (i.e., P 2 and P 4 ) in Equation (1) are always fixed to be 1, and accordingly, there is a total of 78 independent coefficients in the RFM [35].…”

Section: P(umentioning

confidence: 99%

“…High-resolution satellite images (with a spatial resolution of several meters or finer) have been widely used in earth and environmental studies [1]. Limited by the unsatisfactory performance of onboard navigation sensors, the direct-georeferencing accuracy of the satellite imagery is commonly worse than several times of the Ground Sampling Distance (GSD) [2,3], which cannot meet the requirement of high-precision remote-sensing applications such as urban 3-D reconstruction [4] and orthophoto production [5].…”

Section: Introductionmentioning

confidence: 99%

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Bias Compensation for Rational Polynomial Coefficients of High-Resolution Satellite Imagery by Local Polynomial Modeling

Shen

et al. 2017

Remote Sensing

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Abstract:The Rational Function Model (RFM) is a widely used generic sensor model for georeferencing satellite images. Owing to inaccurate measurement of satellite orbit and attitude, the Rational Polynomial Coefficients (RPCs) provided by image vendors are commonly biased and cannot be directly used for high-precision remote-sensing applications. In this paper, we propose a new method for the bias compensation of RPCs using local polynomial models (including the local affine model and the local quadratic model), which provides the ability to correct non-rigid RPC deformations. Performance of the proposed approach was evaluated using a stereo triplet of ZY-3 satellite images and compared with conventional global-polynomial-based models (including the global affine model and the global quadratic model). The experimental results show that, when the same polynomial form was used, the correction residuals of the local model could be notably smaller than those of the global model, which indicates that the new method has great ability to remove complex errors existed in vendor-provided RPCs. In the experiments of this study, the accuracy of the local affine model was nearly 15% better than that of the global affine model. Performance of the local quadratic model was not as good as the local affine model when the number of Ground Control Points (GCPs) was less than 10, but it improved rapidly with an increase in the number of redundant observations. In the test scenario with 15 GCPs, the accuracy of the local quadratic model was about 9% and 27% better than those of the local affine model and the global quadratic model, respectively.

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“…EMOTE sensing technologies have evolved greatly since the launch of the first satellite sensors, with a significant change being the wide suite of very fine spatial resolution (VFSR) sensors borne by diverse platforms (satellite, manned aircraft or unmanned aerial vehicles UAV) [1]. These technical advances have resulted in immense growth in the available VFSR remotely sensed imagery typically acquired at sub-metre spatial resolution [2], such as QuickBird, GeoEye-1, Pleiades-1, and WorldView-2, 3, and 4.…”

Section: Introductionmentioning

confidence: 99%

VPRS-Based Regional Decision Fusion of CNN and MRF Classifications for Very Fine Resolution Remotely Sensed Images

Zhang

Sargent

Pan

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Very High-Resolution Remote Sensing: Challenges and Opportunities [Point of View]

Cited by 93 publications

References 0 publications

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

Screening Mississippi River Levees Using Texture-Based and Polarimetric-Based Features from Synthetic Aperture Radar Data

Bias Compensation for Rational Polynomial Coefficients of High-Resolution Satellite Imagery by Local Polynomial Modeling

VPRS-Based Regional Decision Fusion of CNN and MRF Classifications for Very Fine Resolution Remotely Sensed Images

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