[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
Earth observation (EO) data featuring considerable and complicated heterogeneity are …
From single-to multi-modal remote sensing imagery interpretation: A survey and taxonomy
Modality is a source or form of information. Through various modal information, humans can
perceive the world from multiple perspectives. Simultaneously, the observation of remote …
perceive the world from multiple perspectives. Simultaneously, the observation of remote …
A robust multimodal remote sensing image registration method and system using steerable filters with first-and second-order gradients
Co-registration of multimodal remote sensing (RS) images (eg, optical, infrared, LiDAR, and
SAR) is still an ongoing challenge because of nonlinear radiometric differences (NRD) and …
SAR) is still an ongoing challenge because of nonlinear radiometric differences (NRD) and …
Robust matching for SAR and optical images using multiscale convolutional gradient features
L Zhou, Y Ye, T Tang, K Nan… - IEEE Geoscience and …, 2021 - ieeexplore.ieee.org
Image matching is a key preprocessing step for the integrated application of synthetic
aperture radar (SAR) and optical images. Due to significant nonlinear intensity differences …
aperture radar (SAR) and optical images. Due to significant nonlinear intensity differences …
LNIFT: Locally normalized image for rotation invariant multimodal feature matching
Severe nonlinear radiation distortion (NRD) is the bottleneck problem of multimodal image
matching. Although many efforts have been made in the past few years, such as the …
matching. Although many efforts have been made in the past few years, such as the …
[图书][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
Advances and opportunities in remote sensing image geometric registration: A systematic review of state-of-the-art approaches and future research directions
Geometric registration is often an accuracy assurance for most remote sensing image
processing and analysis, such as image mosaicking, image fusion, and time-series analysis …
processing and analysis, such as image mosaicking, image fusion, and time-series analysis …
Deep learning for processing and analysis of remote sensing big data: A technical review
In recent years, the rapid development of Earth observation technology has produced an
increasing growth in remote sensing big data, posing serious challenges for effective and …
increasing growth in remote sensing big data, posing serious challenges for effective and …
Multimodal hyperspectral remote sensing: An overview and perspective
Since the advent of hyperspectral remote sensing in the 1980s, it has made important
achievements in aerospace and aviation field and been applied in many fields …
achievements in aerospace and aviation field and been applied in many fields …
Automatic registration of optical and SAR images via improved phase congruency model
In this article, we propose an automatic and efficient method to solve optical and synthetic
aperture radar (SAR) image registration using the improved phase congruency (PC) model …
aperture radar (SAR) image registration using the improved phase congruency (PC) model …