X Cheng, Y Sun, W Zhang, Y Wang, X Cao, Y Wang - Remote Sensing, 2023 - mdpi.com
The rapid advancement of remote sensing technology has significantly enhanced the temporal resolution of remote sensing data. Multitemporal remote sensing image …
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficult or impossible to acquire. Self-supervision is a …
Monitoring snow depth is important for applications such as hydrology, energy planning, ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate …
In the realm of conventional deep-learning-based pan-sharpening approaches, there has been an ongoing struggle to harmonize the input panchromatic (PAN) and multi-spectral …
Over large areas, land cover classification has conventionally been undertaken using satellite time series. Typically temporal metric percentiles derived from single pixel location …
Y Huang, GG Yen, VS Tseng - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
Early time series classification predicts the class label of a given time series before it is completely observed. In time-critical applications, such as arrhythmia monitoring in ICU …
M Alami Machichi, E mansouri, Y Imani… - … Journal of Remote …, 2023 - Taylor & Francis
The ever-increasing global population presents a looming threat to food production. To meet growing food demands while minimizing negative impacts on water and soil, agricultural …
X Wan, L Cen, X Chen, Y Xie… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Early classification predicts the class of the incoming sequences before it is completely observed. How to quickly classify streaming time series without losing interpretability …
For Landsat land cover classification, the time series observations are typically irregular in the number of observations in a period (eg, a year) and acquisition dates due to cloud cover …