Oil spill detection using in Sentinel-1 satellite images based on Deep learning concepts

Oil spill detection by satellite SAR

Volumetric soil moisture estimation using Sentinel 1 and 2 satellite images

Surface soil moisture is an important variable that plays a crucial role in the management of water and soil resources. Estimating this parameter is one of the important applications of remote sensing. One of the remote sensing techniques for precise estimation of this parameter is data-driven models. In this study, volumetric soil moisture content was estimated using data-driven models, suppor...

Using deep learning to pansharpen satellite images

Context: The latest generation of satellite-based imaging sensors (Pleiades, Sentinel, etc.) acquires big volumes of Earth’s images with high spatial, spectral and temporal resolution (up to 50cm/pixel, 50 bands, twice per day, covering the full planet!). These data open the door to a large range of important applications, such as the planning of urban environments and the monitoring of natural...

Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images

Polarimetric synthetic aperture radar (SAR) remote sensing provides an outstanding tool in oil spill detection and classification, for its advantages in distinguishing mineral oil and biogenic lookalikes. Various features can be extracted from polarimetric SAR data. The large number and correlated nature of polarimetric SAR features make the selection and optimization of these features impact o...

Early detection of MS in fMRI images using deep learning techniques

Introduction & Objective:MS is a disease of the central nervous system in which the body makes a defensive attack on its tissues. The disease can affect the brain and spinal cord, causing a wide range of potential symptoms, including balance, movement and vision problems. MRI and fMRI images are a very important tool in the diagnosis and treatment of MS. The aim of this study was to provide...

Crop Land Change Monitoring Based on Deep Learning Algorithm Using Multi-temporal Hyperspectral Images

Change detection is done with the purpose of analyzing two or more images of a region that has been obtained at different times which is Generally one of the most important applications of satellite imagery is urban development, environmental inspection, agricultural monitoring, hazard assessment, and natural disaster. The purpose of using deep learning algorithms, in particular, convolutional ...