Object detection in remote sensing images (RSIs) requires the locating and classifying of
objects of interest, which is a hot topic in RSI analysis research. With the development of …

Because of sensor malfunction and poor atmospheric conditions, there is usually a great
deal of missing information in optical remote sensing data, which reduces the usage rate …

The identification of small land targets in remote sensing imagery has emerged as a
significant research objective. Despite significant advancements in object detection …

Remote sensing from unmanned aircraft systems (UAS) was expected to be an important
new technology to assist farmers with precision agriculture, especially crop nutrient …

This paper reviewed major remote sensing image classification techniques, including pixel-
wise, sub-pixel-wise, and object-based image classification methods, and highlighted the …

Thick cloud and its shadow severely reduce the data usability of optical satellite remote
sensing data. Although many approaches have been presented for cloud and cloud shadow …

Since the scan line corrector (SLC) of the Landsat Enhanced Thematic Mapper Plus (ETM+)
sensor failed permanently in 2003, about 22% of the pixels in an SLC-off image are not …

Despite an ever-increasing number of spaceborne, airborne, and ground-based data
acquisition platforms, remote sensing data are still often spatially incomplete or temporally …

Spectral unmixing is the process of decomposing the spectral signature of a mixed pixel into
a set of endmembers and their corresponding abundances. Endmembers are spectra of the …

Cloud cover is generally present in remotely sensed images, which limits the potential of the
images for ground information extraction. Therefore, removing the clouds and recovering the …