Atmospheric correction is one of the key parts of remote sensing preprocessing because it can influence and change the final classification result. This research examines the impact of five different atmospheric correction processing on land cover classification accuracy using Sentinel-2 satellite imagery. Those are surface reflectance (SREF), standardized surface reflectance (STDSREF), Sentinel-2 atmospheric correction (S2AC), image correction for atmospheric effects (iCOR), dark object subtraction (DOS) and top of the atmosphere (TOA) reflectance without any atmospheric correction. Sentinel-2 images corrected with stated atmospheric corrections were classified using four different machine learning classification techniques namely extreme gradient boosting (XGB), random forests (RF), support vector machine (SVM) and catboost (CB). For classification, five different classes were used: bare land, low vegetation, high vegetation, water and built-up area. SVM classification provided the best overall result for twelve dates, for all atmospheric corrections. It was the best method for both cases: when using Sentinel-2 bands and radiometric indices and when using just spectral bands. The best atmospheric correction for classification with SVM using radiometric indices is S2AC with the median value of 96.54% and the best correction without radiometric indices is STDSREF with the median value of 96.83%.
Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.
This paper presents current research done under the GEMINI (Geospatial monitoring of green infrastructure using terrestrial, airborne and satellite imagery) project. GEMINI is a scientific project of the Faculty of Geodesy, University of Zagreb and Croatian Forest Research Institute funded by the Croatian science foundation. The project aims to explore new knowledge about the green infrastructure (GI) monitoring. The study area of the research is the capital of Croatia, the city of Zagreb. One of the main goals of the project is to explore the possibilities of data integration of all available remote sensing platforms: satellite, aerial and UAV (Unmanned Aerial Vehicles) imagery, terrestrial imagery and ground truth data. UAV-based remote sensing offers great possibilities to acquire field data for monitoring of GI within the urban areas in a fast and easy way. The main objective of this research is to establish an innovative, multidimensional system for monitoring of urban green infrastructure. It will integrate the latest means of data collection (multispectral satellite imagery improved and calibrated with high resolution terrestrial and airborne multispectral sources), advanced spatial analysis with the aim to improve decision support system for better management of urban GI. This project will improve the current state of the inventory and monitoring of the urban GI to support decision making and preservation of GI benefits, through the establishment of comprehensive procedures for integration of different sources of imagery at different resolution scales: satellite, terrestrial and airborne. This research will focus on novel methods for GI monitoring on the integration of different multispectral imagery sensors. It will require a highly multidisciplinary approach, leading to improvements in methods and procedures for automatic and semi-automatic processing of a large number of geospatial imagery data in the field of urban forest management with expected impact in forestry, arboriculture, urban and geospatial science.
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