The proceedings contain 45 papers. The topics discussed include: automatic extraction and change monitoring of fire disaster event based on high-resolution nighttime light remote sensing images; a comparison on the use of different satellite multispectral data for the prediction of aboveground biomass; infrastructure monitoring using SAR and multispectral multitemporal images; infrastructure monitoring using SAR and multispectral multi-temporal images; exploring the MSER-based hyperspectral remote sensing image registration; change detection in UWB VHF SAR images exploiting flight heading diversity through robust principal component analysis; impact of a spatial decorrelation of the noise on the estimation accuracy of temporal changes in the scene from a couple of single-look SAR images; an approach to improve detection in scenes with varying object densities in remote sensing; and detection of oil wells based on faster R-CNN in optical satellite remote sensing images.
Image and Signal Processing for Remote Sensing XXVI / Bruzzone, L.; Bovolo, F.; Santi, E.. - STAMPA. - 11533:(2020).
Image and Signal Processing for Remote Sensing XXVI
L. Bruzzone;F. Bovolo;
2020-01-01
Abstract
The proceedings contain 45 papers. The topics discussed include: automatic extraction and change monitoring of fire disaster event based on high-resolution nighttime light remote sensing images; a comparison on the use of different satellite multispectral data for the prediction of aboveground biomass; infrastructure monitoring using SAR and multispectral multitemporal images; infrastructure monitoring using SAR and multispectral multi-temporal images; exploring the MSER-based hyperspectral remote sensing image registration; change detection in UWB VHF SAR images exploiting flight heading diversity through robust principal component analysis; impact of a spatial decorrelation of the noise on the estimation accuracy of temporal changes in the scene from a couple of single-look SAR images; an approach to improve detection in scenes with varying object densities in remote sensing; and detection of oil wells based on faster R-CNN in optical satellite remote sensing images.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione



