Comparison of Various Polarimetric Decomposition Techniques for Crop Classification

Srikanth, P.; Ramana, K. V.; Deepika, U.; Chakravarthi, P. Kalyan; Sai, M. V. R. Sesha

doi:10.1007/s12524-015-0525-6

Cited by 13 publications

(2 citation statements)

References 11 publications

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“…surface, dihedral and volume) (Singh and Yamaguchi, 2018;Xie et al, 2016;Yamaguchi et al, 2005;Cui et al, 2014). However, decomposition methods are commonly used for qualitative analyses, such as image interpretation and classification (Srikanth et al, 2016;Whelen and Siqueira, 2017;Zhao et al, 2014), but not for quantitative applications (van Zyl et al, 2011). For instance, in cases where the ratios of the scattering components from the different decompositions are similar, the classification results may be the same, but the SM estimates would differ greatly because of the inaccurate ground scattering power.…”

Section: Introductionmentioning

confidence: 99%

Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Shi

Zhao

López-Sánchez

et al. 2021

Remote Sensing of Environment

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Surface soil moisture (SM) retrieval over agricultural areas from polarimetric synthetic aperture radar (PolSAR) has long been restricted by vegetation attenuation, simplified polarimetric scattering modelling, and limited SAR measurements. This study proposes a modified polarimetric decomposition framework to retrieve SM from multi-incidence and multitemporal PolSAR observations. The framework is constructed by combining the X-Bragg model, the extended double Fresnel scattering model and the generalised volume scattering model (GVSM). Compared with traditional decomposition models, the proposed framework considers the depolarisation of dihedral scattering and the diverse vegetation contribution. Under the assumption that SM is invariant for the PolSAR observations at two different incidence angles and that vegetation scattering does not change between two consecutive measurements, analytical parameter solutions, including the dielectric constant of soil and crop stem, can be obtained by solving multivariable nonlinear equations. The proposed framework is applied to the time series of L-band uninhabited aerial vehicle synthetic aperture radar data acquired during the Soil Moisture Active Passive Validation Experiment in 2012. In this study, we assess retrieval performance by comparing the inversion results with in-situ measurements over bean, canola, corn, soybean, wheat and winter wheat areas and comparing the different performance of SM retrieval between the GVSM and Yamaguchi volume scattering models. Given that SM estimation is inherently influenced by crop phenology and empirical parameters which are introduced in the scattering models, we also investigate the influence of surface depolarisation angle and co-pol phase difference on SM estimation. Results show that the proposed retrieval framework This is a previous version of the article published in Remote Sensing of Environment. 2021, 261: 112485.

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Section: Introductionmentioning

confidence: 99%

Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Shi

Zhao

López-Sánchez

et al. 2021

Remote Sensing of Environment

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show abstract

“…These methods are proven to be greatly useful in improving land cover classification accuracy (Cloude & Pottier, 1997;Lee et al, 1999;Qi et al, 2012;B. Liu et al, 2013;Srikanth et al, 2016;Subhadip Dey et al, 2020). Mohammadimanesh et al (2018) already analyzed multi-temporal, multi-frequency, and multipolarization coherence and SAR backscatter analysis of wetlands.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the capability of multitemporal RADARSAT-2 fully polarimetric SAR images for land cover classification: a case of Panyu, Guangdong province

Liu

Zhang

et al. 2021

European Journal of Remote Sensing

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Synthetic aperture radar (SAR), with all-day and all-weather observation capabilities, can capture the phenology of crops with short growth cycles to improve land cover classification results. The present study carried out a land cover classification using multitemporal RADARSAT-2 fully polarimetric SAR (PolSAR) images that are 24 days apart. The objectives of this study were 1) examining the land cover classification capacity of multitemporal fully PolSAR data processed with multiple polarimetric decomposition methods, 2) investigating the contribution of multi-type polarimetric decomposition methods to multitemporal PolSAR image classification, 3) and determining optimal image acquisition dates and polarimetric parameters for land cover classification. Overall accuracy and kappa coefficient attained using the multitemporal PolSAR data were 96.55% and 0.96, respectively, which were improved by as much as 16.77% and 0.20, respectively, compared with those obtained with a single scene. Compared with the multitemporal PolSAR image classification using coherency matrices alone, the use of polarimetric decomposition methods improved overall accuracy and kappa value by 2.22% and 0.03, respectively. Using decision tree algorithms for feature selection, we found that April 14, May 8, June 1, and June 25 and Pauli, Cloude, Neumann3, An&Yang4, Freeman3, Barnes2, and MCSM5 decomposition methods were optimal for the land cover classification.

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Classification of Dry and Wet Snow of Glaciers in Western Himalayas Using SAR Polarimetric Decomposition—An Application of Satellite Image Processing

Neha,

Priya,

Jefflin

2024

Lecture Notes in Electrical Engineering

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Comparison of Various Polarimetric Decomposition Techniques for Crop Classification

Cited by 13 publications

References 11 publications

Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques

Investigation of the capability of multitemporal RADARSAT-2 fully polarimetric SAR images for land cover classification: a case of Panyu, Guangdong province

Classification of Dry and Wet Snow of Glaciers in Western Himalayas Using SAR Polarimetric Decomposition—An Application of Satellite Image Processing

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