A fast automatic U-distribution segmentation algorithm for polsar images

Hu, Dingsheng; Doulgeris, Anthony P.; Qiu, Xiaolan; Lei, Bin

doi:10.1109/igarss.2016.7730656

Cited by 3 publications

(2 citation statements)

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“…PolSAR can obtain multidimensional data that describe polarization scattering information of targets. And it has many important applications in target classification [ 1 , 2 ], target detection [ 3 , 4 ], biomass inversion [ 5 , 6 ] and so on. Many countries have paid attention to the development of PolSAR systems, and have launched a series of space-borne PolSAR systems, such as Radarsat-2, TerraSAR-X, ALOS-2, Sentinel-1 and Cosmo-Skymed.…”

Section: Introductionmentioning

confidence: 99%

A Quality Assessment Method Based on Common Distributed Targets for GF-3 Polarimetric SAR Data

Jiang

Qiu

Han

et al. 2018

Sensors

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The GaoFen-3 (GF-3) satellite, launched on 10 August 2016, is the first C-band polarimetric synthetic aperture radar (PolSAR) satellite in China. The PolSAR system of GF-3 can collect a significant wealth of information for geophysical research and applications. Being used for related applications, GF-3 PolSAR images must be of good quality. It is necessary to evaluate the quality of polarimetric data and achieve the normalized quality monitoring during 8-year designed life of GF-3. In this study, a new quality assessment method of PolSAR data based on common distributed targets is proposed, and the performance of the method is analyzed by simulations and GF-3 experiments. We evaluate the quality of GF-3 PolSAR data by this method. Results suggest that GF-3 antenna is highly isolated, and the quality of calibrated data satisfies the requests of quantitative applications.

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Section: Introductionmentioning

confidence: 99%

A Quality Assessment Method Based on Common Distributed Targets for GF-3 Polarimetric SAR Data

Jiang

Qiu

Han

et al. 2018

Sensors

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“…The long processing time is probably the main reason that these non-Gaussian clustering models have not attained more widespread use, since they have been generally demonstrated to have measurably better segmentation results. This paper describes some new ideas to re-parameterise the equations to produce smoothly varying functions that are amenable to low order approximation and fast evaluation with look-up-tables (LUT), and goes beyond those mentioned previously [10,11]. Firstly, the U-distribution expression is shown to be smoothly varying with respect to a compound observable term and may be approximated with linear or cubic spline interpolation for use in a look-up-table.…”

Section: Introductionmentioning

confidence: 99%

Speeding up Non-Gaussian POLSAR image analysis

Doulgeris

2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Non-Gaussian statistical models fit SAR data better than Gaussian-based statistics, in most cases, but are complicated and time-consuming to use for unsupervised image segmentation via probabilistic clustering. The more advanced the model, the more complicated and slow the clustering. The U-distribution has been demonstrated to be one of the most flexible models, capturing the Gaussian/Wishart, the K-distribution and the G 0 models as special cases, but can take hours or days to process a full sized image, depending on the chosen sensitivity. This work explains some computational improvements that drastically reduces the processing time, whilst maintaining the segmented results. The efficiencies are obtained by some smart re-parameterisations of the distribution model parameters allowing the use of lookup-tables for the density function evaluation and parameter estimation. Real images and computation times are shown as a demonstration.

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