Search results for " satellite images"

Using optical satellite and aerial imagery for automatic coastline mapping

2020

The continuous availability and rapid accessibility to multispectral data from satellite platforms within the Copernicus Programme represents a great opportunity for users in different fields of applications as: Agriculture, observation of coastal zones, monitoring land cover change. The aim of this paper is to identify a suitable method to map coastline using Sentinel-2 optical satellite image. The method provides the use of two indexes developed in remote sensing field for water environment: NDWI (Normalized difference water index) and MNDWI (Modified Normalized difference water index). Starting from the construction of maps of these indexes and, identifying appropriate threshold values, …

Methods and Techniques for Multi-source Data Analysis and Fusion

This work has been inspired by the recent trend in remote sensing and environmental data acquisition. Remote sensing techniques allow us to measure information about an object without touching it. In the last decades remote sensing via satellites has been used in various applications such as Earth observation, weather and storm predictive analysis, atmospheric monitoring, climate change, human-environment interactions. Sensors on airborne and satellite platforms have been recording signals from space for many years, giving rise to a huge amount of data. Some data are processed on-board but others are treated and post-processed in ground stations. Signal and image processing are widely appli…

Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Int…

2022

Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining…