A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables

Mishra, Pooja; Singh, Dharmendra

doi:10.1109/tgrs.2013.2267548

Cited by 61 publications

(28 citation statements)

References 29 publications

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“…Most SAR classification algorithms use fixed polarimetric indices to detect certain land cover types, despite the large natural variability between observation sites, temporal acquisition, environmental conditions and calibration effects. To improve on previous approaches, a decision-tree-based adaptive land cover classification technique has been developed [14].…”

Section: Introductionmentioning

confidence: 99%

Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests

et al. 2015

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Abstract:The European CORINE land cover mapping scheme is a standardized classification system with 44 land cover and land use classes. It is used by the European Environment Agency to report large-scale land cover change with a minimum mapping unit of 5 ha every six years and operationally mapped by its member states. The most commonly applied method to map CORINE land cover change is by visual interpretation of optical/near-infrared satellite imagery. The Sentinel-1A satellite carries a C-band Synthetic Aperture Radar (SAR) and was launched in 2014 by the European Space Agency as the first operational Copernicus mission. This study is the first investigation of Sentinel-1A for CORINE land cover mapping. Two of the first Sentinel-1A images acquired during its ramp-up phase in May and December 2014 over Thuringia in Germany are analysed. 27 hybrid level 2/3 CORINE classes are defined. 17 of these were present at the study site and classified based on a stratified random sample of training pixels from the polygon-eroded CORINE 2006 map. Sentinel-1A logarithmic radar backscatter at HH and HV polarisation (May acquisition), VV and VH polarisation (December acquisition), and the HH image texture are used as input bands to the classification. In addition, a Digital Terrain Model OPEN ACCESSRemote Sens. 2015, 7 14877(DTM), a Canopy Height Model (CHM) and slope and aspect maps from the Shuttle Radar Topography Mission (SRTM) are used as input bands to account for geomorphological features of the landscape. In future, elevation data will be delivered for areas with sufficiently high coherence from the Sentinel-1A Interferometric Wide-Swath Mode itself. When augmented by elevation data from radar interferometry, Sentinel-1A is able to discriminate several CORINE land cover classes, making it useful for monitoring of cloud-covered regions. A bistatic Sentinel-1 Convoy mission would enable single-pass interferometric acquisitions without temporal decorrelation.

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Section: Introductionmentioning

confidence: 99%

Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests

et al. 2015

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“…Polarimetric SAR measures radar backscattering from multi-polarization channels, which can provide a number of parameters representing various physical properties of a target that have been widely used for land cover classification [29][30][31]. Several studies demonstrated that a co-polarization ratio (the ratio of backscattering derived from VV and HH polarization), one of the polarimetric parameters, can identify melt ponds on first-year sea ice due to an obvious contrast of the dielectric Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Han

Kim

et al. 2016

Remote Sensing

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Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches-decision trees (DT) and random forest (RF)-in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15ˆ15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.

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“…These datasets contain backscattered intensity values calibrated in terms of backscattering coefficients researchers have found that a particular land cover class is highlighted by these ratio features [11]. Linear combinations of HH, HV and VV (e.g.…”

Section: B Polsar Featuresmentioning

confidence: 99%

“…For instance, σ hh is chosen to be (Table 1). Physical significance of some of these features is summarized in [11] (the list is not exhaustive due to still unknown nature of these complex phenomena). Differentiates single-and multiple-bounce Discriminates bare soil and vegetation…”

Section: B Polsar Featuresmentioning

confidence: 99%

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Critical analysis of classification techniques for polarimetric synthetic aperture radar data

Mittal

Singh

Saini

2016

Int. J. Adv. Intell. Informatics

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Full polarimetry SAR data known as PolSAR contains information in terms of microwave energy backscattered through different scattering mechanisms (surface-, double- and volume-scattering) by the targets on the surface of land. These scattering mechanisms information is different in different features. Similarly, different classifiers have different capabilities as far as identification of the targets corresponding to these scattering mechanisms. Extraction of different features and the role of classifier are important for the purpose of identifying which feature is the most suitable with which classifier for land cover classification. Selection of suitable features and their combinations have always been an active area of research for the development of advanced classification algorithms. Fully polarimetric data has its own advantages because its different channels give special scattering feature for various land cover. Therefore, first hand statistics HH, HV and VV of PolSAR data along with their ratios and linear combinations should be investigated for exploring their importance vis-à-vis relevant classifier for land management at the global scale. It has been observed that individually first hand statistics yield low accuracies. And their ratios are also not improving the results either. However, improved accuracies are achieved when these natural features are stacked together.

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A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables

Cited by 61 publications

References 29 publications

Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests

Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Critical analysis of classification techniques for polarimetric synthetic aperture radar data

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