[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 …
Deep learning meets hyperspectral image analysis: A multidisciplinary review
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …
abundance of information; such a resource, however, poses many challenges in the …
[HTML][HTML] A survey: Deep learning for hyperspectral image classification with few labeled samples
With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …
Hyperspectral and SAR image classification via multiscale interactive fusion network
Due to the limitations of single-source data, joint classification using multisource remote
sensing data has received increasing attention. However, existing methods still have certain …
sensing data has received increasing attention. However, existing methods still have certain …
Coupled adversarial learning for fusion classification of hyperspectral and LiDAR data
Hyperspectral image (HSI) provides rich spectral–spatial information and the light detection
and ranging (LiDAR) data reflect the elevation information, which can be jointly exploited for …
and ranging (LiDAR) data reflect the elevation information, which can be jointly exploited for …
Fusatnet: Dual attention based spectrospatial multimodal fusion network for hyperspectral and lidar classification
With recent advances in sensing, multimodal data is becoming easily available for various
applications, especially in remote sensing (RS), where many data types like multispectral …
applications, especially in remote sensing (RS), where many data types like multispectral …
Hyperspectral and lidar data applied to the urban land cover machine learning and neural-network-based classification: A review
Rapid technological advances in airborne hyperspectral and lidar systems paved the way
for using machine learning algorithms to map urban environments. Both hyperspectral and …
for using machine learning algorithms to map urban environments. Both hyperspectral and …
Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data
X Wang, Y Feng, R Song, Z Mu, C Song - Information Fusion, 2022 - Elsevier
With recent advance in Earth Observation techniques, the availability of multi-sensor data
acquired in the same geographical area has been increasing greatly, which makes it …
acquired in the same geographical area has been increasing greatly, which makes it …
Land cover classification from fused DSM and UAV images using convolutional neural networks
In recent years, remote sensing researchers have investigated the use of different modalities
(or combinations of modalities) for classification tasks. Such modalities can be extracted via …
(or combinations of modalities) for classification tasks. Such modalities can be extracted via …
From local to regional compound flood map** with deep learning and data fusion techniques
Compound flooding (CF), as a result of oceanic, hydrological, meteorological and
anthropogenic drivers, is often studied with hydrodynamic models that combine either …
anthropogenic drivers, is often studied with hydrodynamic models that combine either …