Classification of full polarimetric SAR-data using artificial neural networks and fuzzy algorithms

Hellmann, Martin; Jager, G.; Kratzschmar, E.; Habermeyer, Martin

doi:10.1109/igarss.1999.775011

Cited by 35 publications

(19 citation statements)

References 3 publications

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“…On base of the diagonalization of the [T] matrix, three important physical parameters arise. The first two parameters are derived from the eigenvalues λ 1 −λ 3 and are namely the Entropy H and the Anisotropy A and give an overview about the amount of different scattering mechanisms within a resolution cell (Hellmann et al, 1999). The third parameter, the polarimetric alpha angle α, is derived from the eigenvectors of the coherency matrix [T], were each eigenvector e i can be expressed in terms of five angles as shown in (5) (Cloude and Pottier 1997).…”

Section: Radar Acquisitions and Processingmentioning

confidence: 99%

“…At its maximum of α= 90 • , one can obtain an isotropic double bounce scattering mechanism (Cloude and Pottier, 1997). As a consequence, the polarimetric alpha angle, jointly used with the Entropy, gives a first impression of the dominant scattering mechanisms (Cloude and Pottier, 1997;Hellmann et al, 1999). Cloude (1999), Cloude and Lewis (2000) as well as Hajnsek et al (2003) first introduced the Anisotropy as a potential roughness estimator, which is only dependent from the geometrical properties of a given surface and independent from its dielectric properties as well as the local incidence angle.…”

Section: Radar Acquisitions and Processingmentioning

confidence: 99%

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On the derivation of soil surface roughness from multi parametric PolSAR data and its potential for hydrological modeling

Marzahn

Ludwig

2009

Hydrol. Earth Syst. Sci.

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Abstract. The potential of multi parametric polarimetric SAR (PolSAR) data for soil surface roughness estimation is investigated and its potential for hydrological modeling is evaluated. The study utilizes microwave backscatter collected from the DEMMIN test site in the North East of Germany during the AgriSAR 2006 campaign using fully polarimetric L-band E-SAR data. In addition to various measurements of soil physical properties, soil surface roughness was measured extensively using photogrammetric image matching techniques for ground truthing. The resulting micro-DSMs are analyzed to correlate a soil surface roughness index to three well established polarimetric roughness estimators. Good results are obtained for Re [ρRRLL] vs. RMS Height for areas with a polarimetric alpha angel α<40 • , which is thus used to produce multi temporal roughness data of the test site. The proposed roughness inversion scheme showed sufficiently accurate results (RMSE=0.1) to allow for a first order assessment of soil-hydrological parameters (soil porosity, void ratio), which are crucial for the initialization and operation of hydrological surface models. While uncertainties remain, the dependency of soil bulk density parameters from surface roughness can be shown and thus highlights the potential of the retrieval approach for hydrological model applications.

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Section: Radar Acquisitions and Processingmentioning

confidence: 99%

Section: Radar Acquisitions and Processingmentioning

confidence: 99%

On the derivation of soil surface roughness from multi parametric PolSAR data and its potential for hydrological modeling

Marzahn

Ludwig

2009

Hydrol. Earth Syst. Sci.

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“…Recently, most works are trying to extract useful features in different prospects or deal with the issue from the prospects of both its physical scatting property and its image characteristics. Polarimetric target decomposition theorem [1] , H and α angle distribution PolSAR image classification [2] [3] , classification method based on statistic classifiers Neural Network (NN) [4] and Support Vector Machine [5] are some typical classification methods. However, most of these methods, though providing acceptable results, are somehow too complicated or timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Polarmetric SAR images classification based on sparse representation theory

Zhang

Chen

et al. 2013

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS

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Feature extraction and image classification using PolSAR images is currently of great interest in SAR applications. On the basis of the sparse characteristics of the features for PolSAR image classification, a supervised PolSAR image classification method based on sparse representation is proposed in this paper, in which the test data can be firstly projected onto a subset of training vectors from the dictionary, then the residual errors with respect to each atom are evaluated and considered as the criteria for classification, and the ultimate class results can be obtained according to the atom with the least residual error. In addition, a Simplified Matching Pursuit (SMP) algorithm is proposed to solve the optimization problem of sparse representation of PolSAR images. The experimental results of Danish EMISAR L-band fully polarimetric SAR data of Foulum Area (DK) confirm our method outputs an excellent result and moreover the classification process is simpler and less time consuming.

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“…Many of the aforementioned algorithms have fuzzy relatives [5], [9], [19], but there are also other approaches using fuzzy set theory [3], [18], [25] and also classification with neural nets is performed [2], [11], [14]. In order to compare these algorithms' with respect to classification accuracy, it is necessary to apply them on the same data set and to evaluate the corresponding accuracy with an objective quantitative method.…”

Section: Introductionmentioning

confidence: 99%