Mohammed Dabboor

An Unsupervised Classification Approach for Polarimetric SAR Data Based on the Chernoff Distance for Complex Wishart Distribution

A new unsupervised classification approach for polarimetric synthetic aperture radar (POLSAR) data is proposed in this paper. The Wishart-Chernoff distance is calculated and used in an agglomerative hierarchical clustering approach. Initial segmentation of POLSAR data into clusters is obtained based on the total backscattering power (SPAN) combined with the entropy, alpha angle, and anisotropy. The complex Wishart clustering is performed to optimize the initialization. Optimized clusters with minimum Wishart-Chernoff distance are merged hierarchically into an appropriate number of classes. The appropriate number of classes is estimated based on the data log-likelihood algorithm. Classification results show that the use of Wishart-Chernoff distance is superior to that of the Wishart test statistic distance. The effectiveness of the proposed Wishart-Chernoff distance is demonstrated using Advanced Land Observing Satellite POLSAR data.

Comparing matrix distance measures for unsupervised POLSAR data classification of sea ice based on agglomerative clustering

Clustering is a technique that can be applied for unsupervised classification of polarimetric synthetic aperture radar (POLSAR) data, an important analysis technique of radar polarimetry. Six matrix distance measures have been investigated and compared through an agglomerative clustering of RADARSAT-2 POLSAR data. The considered matrix distance measures were used as similarity criteria for merging clusters hierarchically into an appropriate number of classes. In this study, the considered distances are Manhattan, Euclidean, Bartlett, revised Wishart, Wishart test statistic, and Wishart Chernoff. Results show that the Bartlett, revised Wishart, and Wishart Chernoff distances all produce identical classification results. The Manhattan distance retrieved classification results close to those obtained by the Euclidean distance. The Bartlett, revised Wishart, and Wishart Chernoff distances produced the most accurate classification results. The study area is located in Hudson Bay offshore Churchill, Manitoba, Canada.

A multi-level segmentation methodology for dual-polarized SAR data

Abstract An innovative methodology for dual-polarized Synthetic Aperture Radar (SAR) data segmentation is proposed. The methodology is based on the thresholding of the 1D-histograms of the two images produced by the dual polarimetric bands. Thresholding of the histograms is performed using a nonparametric algorithm. Histograms after thresholding are combined together in a two dimensional histogram-based space in order to define sub-spaces, which are used for image segmentation. Sub-spaces are further divided based on two criteria which lead to a multi-level segmentation approach. Dual-polarized TerraSAR-X data, both HH and VV, are used in a study area located in the southwestern United Kingdom.

Improving Sea Ice Characterization in Dry Ice Winter Conditions Using Polarimetric Parameters from C- and L-Band SAR Data

Sea ice monitoring and classification is one of the main applications of Synthetic Aperture Radar (SAR) remote sensing. C-band SAR imagery is regarded as an optimal choice for sea ice applications; however, other SAR frequencies has not been extensively assessed. In this study, we evaluate the potential of fully polarimetric L-band SAR imagery for monitoring and classifying sea ice during dry winter conditions compared to fully polarimetric C-band SAR. Twelve polarimetric SAR parameters are derived using sets of C- and L-band SAR imagery and the capabilities of the derived parameters for the discrimination between First Year Ice (FYI) and Old Ice (OI), which is considered to be a mixture of Second Year Ice (SYI) and Multiyear Ice (MYI), are investigated. Feature vectors of effective C- and L-band polarimetric parameters are extracted and used for sea ice classification. Results indicate that C-band SAR provides high classification accuracy (98.99%) of FYI and OI in comparison to the obtained accuracy using L-band SAR (82.17% and 81.85%), as expected. However, L-band SAR was found to classify only the MYI floes as OI, while merging both FYI and SYI into one separate class. This comes in contrary to C-band SAR, which classifies as OI both MYI and SYI. This indicates a new potential for discriminating SYI from MYI by combining C- and L-band SAR in dry ice winter conditions.

Land Cover Segmentation of ALOS Polarimetric SAR Data

Image segmentation is a basic step of any segment-based classification method. Various segmentation approaches of polarimetric SAR data, such as region growing and splitmerge to name a few, have been proposed recently. This paper describes the development of a new segmentation approach that improves the polarimetric SAR data analysis by including information from the backscattering behavior of objects in the Freeman-Durden analysis images. This method is based on the main scattering mechanism that appears in each image pixel and the second most important scattering mechanism that might have been contributed significantly in the scattering process. Further segmentation is performed based on the calculated histograms of sub-regions. The state-of-art ALOS polarimetric SAR data are used in this study. The study area is located in the south of the United Kingdom and includes the city of Minehead.

Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization

Simulated compact polarimetry from the RADARSAT Constellation Mission (RCM) is evaluated for sea ice classification. Compared to previous studies that evaluated the potential of RCM for sea ice classification, this study focuses on the High Resolution (HR) Synthetic Aperture Radar (SAR) mode of the RCM associated with a higher noise floor (Noise Equivalent Sigma Zero of −19 dB), which can prove challenging for sea ice monitoring. Twenty three Compact Polarimetric (CP) parameters were derived and analyzed for the discrimination between first year ice (FYI) and multiyear ice (MYI). The results of the RCM HR mode are compared with those previously obtained for other RCM SAR modes for possible CP consistency parameters in sea ice classification under different noise floors, spatial resolutions, and radar incidence angles. Finally, effective CP parameters were identified and used for the classification of FYI and MYI using the Random Forest (RF) classification algorithm. This study indicates that, despite the expected high noise floor of the RCM HR mode, CP SAR data from this mode are promising for the classification of FYI and MYI in dry ice winter conditions. The overall classification accuracies of CP SAR data over two test sites (96.13% and 96.84%) were found to be comparable to the accuracies obtained using Full Polarimetric (FP) SAR data (98.99% and 99.20%).

Correction: Assessment of the High Resolution SAR Mode of the RADARSAT Constellation Mission for First Year Ice and Multiyear Ice Characterization. Remote Sensing 2018, 10, 594

Mohammed Dabboor 1,* , Benoit Montpetit 2 and Stephen Howell 3 1 Meteorological Research Division, Environment and Climate Change Canada, Government of Canada, Dorval, QC H9P 1J3, Canada 2 Landscape Science and Technology Division, Environment and Climate Change Canada, Government of Canada, Ottawa, ON K1A 0H3, Canada; Benoit.Montpetit2@canada.ca 3 Climate Research Division, Environment and Climate Change Canada, Government of Canada, Toronto, ON M3H 5T4, Canada; Stephen.Howell@canada.ca * Correspondence: Mohammed.Dabboor@canada.ca; Tel.: +1-514-421-4756

Multiscale Segmentation of Polarimetric SAR Data Using Pauli Analysis Images

Image segmentation is a crucial process that affects the output of any segment-based classification method and governs the interpretation process. There are many approaches for segmentation of polarimetric SAR data, such as region growing and split-merge to name a few, that have been proposed recently. This paper presents the development of a new segmentation approach based on the dominant scattering mechanisms that contribute to the backscattering process, using Pauli analysis images as input data. After accomplishing the segmentation based on the scattering mechanism, further segmentation is performed by the calculation and segmentation of histograms into homogeneous regions. State-of-art ALOS polarimetric SAR data are used in the study area which is located in the southern United Kingdom and includes the city of Minehead.