Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …

Earthquakes are among the most catastrophic natural disasters to affect mankind. One of the
critical problems after an earthquake is building damage assessment. The area, amount …

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and
achieved good performance. In general, deep models adopt a large number of hierarchical …

Building extraction is a challenging research direction in remote sensing image (RSI)
interpretation. Due to the fact that a building has not only its own local structures but also …

Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from
more and more researchers. HSI has abundant spectral and spatial information; thus, how to …

Spectral–spatial features are important for ground target identification and classification with
high spatial resolution remotely sensed (HSRRS) Imagery. In this article, two novel features …

Abstract Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-
weather, day and night observation and high resolution capabilities. The collected data are …

Recent advances in spectral-spatial classification of hyperspectral images are presented in
this paper. Several techniques are investigated for combining both spatial and spectral …

It is widely agreed that spatial features can be combined with spectral properties for
improving interpretation performances on very-high-resolution (VHR) images in urban …

The integration of spatial context in the classification of hyperspectral images is known to be
an effective way in improving classification accuracy. In this paper, a novel spectral-spatial …