Urban Satellite Imagery Analysis

Overview

This page consolidates a group of related research contributions on urban satellite imagery analysis, with a focus on SAR segmentation, scattering estimation, spectral mapping, and urban classification.

The goal is to provide one clean public reference point for resume and portfolio use instead of listing multiple long publication links in a single place.

Scope of Work

• Semantic segmentation of urban areas in polarimetric SAR imagery using deep neural networks and decision trees.
• Optimized estimation of surface double-bounce and volumetric scattering using polarimetric orientation and inclination angles.
• Spectral mapping and tracking error analysis for signal-processing workflows.
• DNN-based urban image segmentation and classification with a focus on practical remote-sensing interpretation.

Linked Publications

1. Modern Mathematical Modelling Approaches for Optimized Estimation of Surface Double Bounce and Volumetric Scattering
2. Semantic Segmentation of Urban Areas in Polarimetric SAR Imaging
3. Spectral Mapping and Tracking Error of MBOC Signal for GNSS
4. DNN-PolSAR Urban Image Segmentation and Classification

Summary

Across these publications, the work centered on practical urban-scene understanding from SAR and related imagery, combining classical signal understanding with deep-learning-driven segmentation and classification workflows.