Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution

Lee, J. S.; Grunes, M.R.; Kwok, R.

doi:10.1080/01431169408954244

Cited by 668 publications

(378 citation statements)

References 10 publications

Supporting

Mentioning

342

Contrasting

Unclassified

Order By: Relevance

“…2(b)). The details of these classification algorithms can be found in literature [6,[10][11][12][13][14][15]18]. We have however discussed the algorithm and methodology of the present classification in brief.…”

Section: Supervised Classification Of Multi-polarization Sar Datamentioning

confidence: 99%

See 1 more Smart Citation

Assessment of L-Band Sar Data at Different Polarization Combinations for Crop and Other Landuse Classification

Haldar¹,

Das²,

Mohan³

et al. 2012

PIER B

View full text Add to dashboard Cite

Abstract-In the present study evaluation of L-band SAR data at different polarization combinations in linear, circular as well as hybrid polarimetric imaging modes for crop and other landuse classifications has been carried out. Full-polarimetric radar data contains all the scattering information for any arbitrary polarization state, hence data of any combination of transmit and receive polarizations can be synthesized, mathematically from full-polarimetric data. Circular and various modes of hybrid polarimetric data (where the transmitter polarization is either circular or orientated at 45 • , called π/4 and the receivers are at horizontal and vertical polarizations with respect to the radar line of sight) were synthesized (simulated) from ALOS-PALSAR fullpolarimetric data of 14th December 2008 over central state farm central latitude and longitude 29 • 15 N/75 • 43 E and bounds for northwest corner is 29 • 24 N/75 • 37 E and southeast corner is 29 • 07 N/75 • 48 E in Hisar, Haryana (India) Supervised classification was conducted for crops and few other landuse classes based on ground truth measurements using maximum-likelihood distance measures derived from the complex Wishart distribution of SAR data at various polarization combinations. It has been observed that linear fullpolarimetric data showed maximum classification accuracy (92%) followed by circular-full (89%) and circular-dual polarimetric data (87%), which was followed by hybrid polarimetric data (73-75%) and then linear dual polarimetric data (63-71%). Among the linear dual polarimetric data, co-polarization complex data showed better classification accuracy than the cross-polarization data. Also multidate single polarization SAR data over central state farm during rabi (winter) season was analyzed and it was observed that single date fullpolarimetric SAR data produced equally good classification result as the multi-date single polarization SAR data.

show abstract

Section: Supervised Classification Of Multi-polarization Sar Datamentioning

confidence: 99%

“…The SAR polarimetric covariance matrix obeys the statistics of a complex multivariate Wishart distribution [12][13][14]. The probability density function of the n-look covariance matrix, Z, is…”

Section: Modementioning

confidence: 99%

Assessment of L-Band Sar Data at Different Polarization Combinations for Crop and Other Landuse Classification

Haldar¹,

Das²,

Mohan³

et al. 2012

PIER B

View full text Add to dashboard Cite

show abstract

“…The simulated PolSAR datasets are generated by using Monte Carlo simulation according to the procedure provided by Lee et al [33]. The given coherent matrices are obtained from the samples of the real PolSAR data, and the regions of different heterogeneity are generated by varying the shape factor in K-distribution [34].…”

Section: Description Of the Experimental Datasetsmentioning

confidence: 99%

Feature-Based Nonlocal Polarimetric SAR Filtering

et al. 2017

View full text Add to dashboard Cite

Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance (CV) and Pauli basis (PB) to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

show abstract

“…Supervised Wishart classifier [2] was used to classify the polarimetric SAR data sets after applying different terrain correction methods. In our study region in the Three Gorges Area, China, there are four main land use and land cover (LU/LC) types.…”

Section: Application Of Sar Terrain Correction To Lu/lc Mappingmentioning

confidence: 99%

“…These applications include land use/land cover mapping [1][2][3][4][5][6][7][8][9], change detection, hazard monitoring and damage assessment, surface geophysical parameters retrieval [10], biomass and forest height estimation, etc. [11].…”

Section: Introductionmentioning

confidence: 99%

An Improved Sar Radiometric Terrain Correction Method and Its Application in Polarimetric Sar Terrain Effect Reduction

Chen¹

2013

PIER B

View full text Add to dashboard Cite

Abstract-A new SAR radiometric terrain correction method was proposed to reduce the terrain effects in sloped regions. Based on this method, a procedure for polarimetric SAR terrain effect reduction was proposed, including geometric correction, shadow detection, radiometric terrain correction, and polarization orientation angle shift compensation. Experiments using RADARSAT-2 polarimetric SAR data of the Three Gorges Area, China demonstrated the effectiveness of the proposed radiometric terrain correction method. Both visual and quantitative analyses showed that after the proposed radiometric terrain correction method was applied, the contrast between different slopes that caused by local incidence angle differences, foreshortening, and layover was significantly reduced. The difference of backscattering intensity on slopes facing the radar sensor and facing away from the sensor was reduced from 12.5 dB before radiometric correction to 1.3 dB. The overall accuracy of land use/land cover classification was improved by 11.2 percent using the terrain corrected polarimetric SAR data.

show abstract

Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution

Cited by 668 publications

References 10 publications

Assessment of L-Band Sar Data at Different Polarization Combinations for Crop and Other Landuse Classification

Assessment of L-Band Sar Data at Different Polarization Combinations for Crop and Other Landuse Classification

Feature-Based Nonlocal Polarimetric SAR Filtering

An Improved Sar Radiometric Terrain Correction Method and Its Application in Polarimetric Sar Terrain Effect Reduction

Contact Info

Product

Resources

About