With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Rapid technological advances in airborne hyperspectral and lidar systems paved the way
for using machine learning algorithms to map urban environments. Both hyperspectral and …

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 …

The excellent performance of graph convolutional networks (GCNs) on non-Euclidean data
has drawn widespread attention from the hyperspectral image classification (HSIC) …

Singular spectral analysis (SSA) has recently been successfully applied to feature extraction
in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D …

Multimodal data fusion, eg, hyperspectral image (HSI) and light detection and ranging
(LiDAR) data fusion, plays an important role in object recognition and classification tasks …

In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of
individual data. Complementary physical contents of data sources allow comprehensive and …

The effective utilization of multimodal data (eg, hyperspectral and light detection and ranging
(LiDAR) data) has profound implications for further development of the remote sensing (RS) …

Remote sensing technology has undeniable importance in various industrial applications,
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …

Deep learning algorithms that can effectively extract features from different modalities have
achieved significant performance in multimodal remote sensing (RS) data classification …