Multispectral Optical Emission Modeling of Sprites Using Plasma Streamer Simulations: A Computational Electromagnetics Approach for Remote Sensing Applications Carlos Gómez∗ and …
Deep learning has become a powerful tool in remote sens-ing, advancing tasks such as classification, segmentation, and regression on multispectral and hyperspectral data [1, 6].
Satellite remote sensing provides a consistent framework for quantifying vegetation greenness via the normalized difference vegetation index (NDVI), a standardized ratio of near-infrared and red …
Abstract tion of single-tree crowns from remote sensing data. However, their segmentation performance still depends on local stand conditions. Using UAV multispectral imagery and lidar canopy height …
remote sensing and image interpretation 7th edition is a cornerstone resource for anyone eager to understand the fascinating field of observing Earth from a distance. Whether you’re a student, a …
SVM is well suited for raster input (single and multiple bands) and has demonstrated good performance in remote sensing image analysis34–36.
Development of a Multispectral Image Database in Visible–Near–Infrared for Demosaicking and Machine Learning Applications