Land use/land cover classification of fused Sentinel-1 and Sentinel-2 imageries using ensembles of Random Forests

The study explores the synergistic combination of Synthetic Aperture Radar (SAR) and Visible-Near Infrared-Short Wave Infrared (VNIR-SWIR) imageries for land use/land cover (LULC) classification. Image fusion, employing Bayesian fusion, merges SAR texture bands with VNIR-SWIR imageries. The research aims to investigate the impact of this fusion on LULC classification. Despite the popularity of random forests for supervised classification, their limitations, such as suboptimal performance with fewer features and accuracy stagnation, are addressed. To overcome these issues, ensembles of random forests (RFE) are created, introducing random rotations using the Forest-RC algorithm. Three rotation approaches: principal component analysis (PCA), sparse random rotation (SRP) matrix, and complete random rotation (CRP) matrix are employed. Sentinel-1 SAR data and Sentinel-2 VNIR-SWIR data from the IIT-Kanpur region constitute the training datasets, including SAR, SAR with texture, VNIR-SWIR, VNIR-SWIR with texture, and fused VNIR-SWIR with texture. The study evaluates classifier efficacy, explores the impact of SAR and VNIR-SWIR fusion on classification, and significantly enhances the execution speed of Bayesian fusion code. The SRP-based RFE outperforms other ensembles for the first two datasets, yielding average overall kappa values of 61.80% and 68.18%, while the CRP-based RFE excels for the last three datasets with average overall kappa values of 95.99%, 96.93%, and 96.30%. The fourth dataset achieves the highest overall kappa of 96.93%. Furthermore, incorporating texture with SAR bands results in a maximum overall kappa increment of 10.00%, while adding texture to VNIR-SWIR bands yields a maximum increment of approximately 3.45%.Comment: Thesis for Master of Technology. Created: July 2018. Total pages 12

Classification of sea ice types for the East part of Greenland waters using SENTINEL 1 data

Ships navigate in Greenland waters all year round. Cruises to Greenland due to tourism and educational purposes have increased the last decade. Hence, it is essential for ships that navigate through Sea Ice in winter to use reliable and accurate information on sea ice conditions. An accurate classification of Sea Ice types is an ongoing problem. Many classification algorithms depend only on the SAR image intensity for discriminating the sea ice types. Different Sea Ice types exhibit similar backscatter signature which makes the algorithm unable to correctly classify them. In this study, two dual-polarization SENTINEL-1 images with a spatial resolution of 40 x 40m acquired over the East part of Greenland in February and May of 2016. Support Vector Machine (SVM) classifier was used to perform the classification. In order to improve the discrimination of ice types, texture analysis was performed using Grey Level Co-occurrence Matrix (GLCM) algorithm. Ten GLCM texture features were calculated. The analysis revealed that the most informative texture features for the sea ice classification are Energy, mean, dissimilarity for HV polarization and Angular Second Moment, variance and energy for HH polarization. The classification results for the SAR images acquired during winter and spring period were compared against the sea ice charts produced by DMI. A good agreement between the classification results and validation data is shown. The results show that the overall classification accuracy for both SAR images amount to 91%. The most hazardous for ships navigation sea ice types (old ice and deformed first year ice) have been successfully discriminated

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought