Comparison Between Spectral, Spatial and Polarimetric Classification of Urban and Periurban Landcover Using Temporal Sentinel – 1 Images

Abstract

ABSTRACT:Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

“…This indicates that these Sentinel-1A-derived images, when using their information in a complementary way, can effectively improve classification accuracy. Some previous studies have also reported on the importance of using the coherence and texture information of SAR data [17,19,40].…”

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

“…While the use of exclusively backscattering 37 coefficients yielded an overall accuracy of less than 50% (Roychowdhury, 2016), more 38 accurate classifications have been possible using a combination of Haralik textures, the 39 polarization ratio and the local mean together with the VV backscattering coefficients 40 (Inglada et al, 2016). However, in some areas (including our study area) there were few 41 opportunities to obtain polarimetric Sentinel-1A data.…”