ABSTRACT A large number of publications have incorporated deep learning in the process
of remote sensing change detection. In these Deep Learning Change Detection (DLCD) …

Preharvest crop yield prediction is critical for grain policy making and food security. Early
estimation of yield at field or plot scale also contributes to high-throughput plant phenotyping …

Texture measurements quantitatively describe relationships of DN values of neighbouring
pixels. The output is a continuous measure of spatial information that may be used for further …

Understory plant communities are an integral component of deciduous forests, playing a
vital role in the overall health of the ecosystem. However, remote sensing of understory plant …

In this paper we study an effective solution to deal with supervised change detection in very
high geometrical resolution (VHR) images. High within-class variance as well as low …

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be
used for N nutrition diagnosis and management. This work aims to explore the potential of …

To improve the accuracy of change detection in urban areas using bi-temporal high-
resolution remote sensing images, a novel object-based change detection scheme …

The use of a fast and accurate unmanned aerial vehicle (UAV) digital camera platform to
estimate leaf area index (LAI) of kiwifruit orchard is of great significance for growth, yield …

Estimating crop yield in large areas is essential for ensuring food security and sustainable
development. Accounting for variations in the temporal cumulative growth of crops across …

Crop plant detection is vital for mapping crop planting area and extracting pure crop canopy
information. In this study, three cameras (RGB, color infrared (NIR-GB) and multispectral …