An improved land cover classification using polarization signatures for PALSAR 2 data

Abstract Land cover classification in mixed land cover scenarios is challenging with PolSAR data. Polarimetric decomposition techniques are most popular methods for PolSAR data classification in recent times. These techniques focus on identification of dominant scattering phenomena and hence result in sub-optimal classification in mixed land cover scenarios. Alternatively, polarization signatures (PSs) are good illustrations of SAR target responses as they depict a detailed physical information from target backscatter. Researchers have successfully utilized SAR PSs for land cover (LC) classification. Some reports suggested utilizing correlation between observed PSs and standard target PSs as features for LC classification. This paper presents a study on improved utilization of PSs for optimal LC classification in mixed class scenarios. First, PS based SAR features are derived using fully polarimetric SAR data. The features represent a degree of similarity between observed and standard PSs. The derived features are termed as polarization signatures correlation features or PSCFs. The novel PSCFs are analyzed, evaluated and compared with decomposition based features for the purpose of LC classification. Classification performance indicators highlight potential of PSCFs for mixed LC classification problems. Therefore, further an adaptive and optimal LC class boundary estimation approach for LC classification is proposed and developed. Observed PSs and reference LC class PS statistics are used to build empirical models between classification performance indicators and LC class boundaries. The empirical models are optimized using the evolutionary genetic algorithm to maximize classification performance. A decision tree is constructed based on the optimal class boundaries to prepare LC classification. The proposed classification approach is compared with some recent popular classifiers and comparison suggests that the proposed approach provides satisfactory results for mixed LC classification scenarios.

[1]  Lijun Lu,et al.  A Multi-component Decomposition Method for Polarimetric SAR Data , 2017 .

[2]  Dharmendra Singh,et al.  An optimal selection of probability distribution functions for unsupervised land cover classification of PALSAR-2 data , 2019, Advances in Space Research.

[3]  Lalit Mohan Saini,et al.  A critical analysis of EM based fusion of different polarization data for effect on land cover classification , 2015 .

[4]  Yoshio Yamaguchi,et al.  Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on SAR Observables , 2011 .

[5]  Akanksha Garg,et al.  Critical Analysis of Model-Based Incoherent Polarimetric Decomposition Methods and Investigation of Deorientation Effect , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Kohei Arai,et al.  Polarimetric SAR image classification with the maximum curvature of the trajectory in the eigen space converted from the polarization signature , 2007 .

[7]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[8]  Albert Guissard,et al.  Mueller and Kennaugh matrices in radar polarimetry , 1994, IEEE Trans. Geosci. Remote. Sens..

[9]  E. Nezry,et al.  On the preservation of polarimetric signatures and polarimetric texture signatures by fully polarimetric MAP filters , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[10]  Yasser Maghsoudi,et al.  A New Method for Land Cover Characterization and Classification of Polarimetric SAR Data Using Polarimetric Signatures , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Avik Bhattacharya,et al.  A Generalized Volume Scattering Model-Based Vegetation Index From Polarimetric SAR Data , 2019, IEEE Geoscience and Remote Sensing Letters.

[12]  Francesca Bovolo,et al.  A Novel Technique Based on Deep Learning and a Synthetic Target Database for Classification of Urban Areas in PolSAR Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Mitchell R. Grunes,et al.  Polarimetric property preservation in SAR speckle filtering , 1997, Optics & Photonics.

[14]  Dharmendra Singh,et al.  A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Feng Qi,et al.  Pol-SAR Classification Based on Generalized Polar Decomposition of Mueller Matrix , 2016, IEEE Geoscience and Remote Sensing Letters.

[16]  R. K. Panigrahi,et al.  Three-component decomposition technique for hybrid-pol SAR data , 2016 .

[17]  Thomas L. Ainsworth,et al.  Generalized Polarimetric Model-Based Decompositions Using Incoherent Scattering Models , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Avik Bhattacharya,et al.  An Adaptive General Four-Component Scattering Power Decomposition With Unitary Transformation of Coherency Matrix (AG4U) , 2015, IEEE Geoscience and Remote Sensing Letters.

[19]  J.J. van Zyl,et al.  Feature Utility In Polarimetric Radar Image Classificatiion , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[20]  Zongben Xu,et al.  An Active Deep Learning Approach for Minimally Supervised PolSAR Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..

[22]  Stanislawa Porzycka-Strzelczyk,et al.  Identification of Coherent Scatterers in SAR Images Based on the Analysis of Polarimetric Signatures , 2014, IEEE Geoscience and Remote Sensing Letters.

[23]  Eric Pottier,et al.  Application of the «H / A / α» Polarimetric Decomposition Theorem for Unsupervised Classification of Fully Polarimetric SAR Data Based on the Wishart Distribution , 2000 .

[24]  N. Shimizu,et al.  Monitoring of long-term land subsidence from 2003 to 2017 in coastal area of Semarang, Indonesia by SBAS DInSAR analyses using Envisat-ASAR, ALOS-PALSAR, and Sentinel-1A SAR data , 2019, Advances in Space Research.

[25]  Eric Rignot,et al.  Potential Applications of Polarimetry to the Classification of Sea Ice , 2013 .

[26]  F. Sadjadi.  Improved target classification using optimum polarimetric SAR signatures , 2002 .

[27]  Lamei Zhang,et al.  Comparison of Methods for Target Detection and Applications Using Polarimetric SAR Image , 2008 .

[28]  Mazlan Hashim,et al.  Structural geology mapping using PALSAR data in the Bau gold mining district, Sarawak, Malaysia , 2014 .

[29]  E. Pottier,et al.  Electromagnetic Vector Scattering Operators , 2017 .