State of the art reviews of remote sensing change detection are becoming increasingly
complicated and disparate due to an ever growing list of techniques, algorithms and …

Crop biomass estimation with high accuracy at low-cost is valuable for precision agriculture
and high-throughput phenotyping. Recent technological advances in Unmanned Aerial …

Applications of remote sensing using unmanned aerial vehicle (UAV) in agriculture has
proved to be an effective and efficient way of obtaining field information. In this study, we …

In this paper we present an approach to perform relative spectral alignment between optical
cross-sensor acquisitions. The proposed method aims at projecting the images from two …

Time-series Vegetation Indices (VIs) are usually used for estimating grain yield. However,
multi-temporal VIs may be affected by different background, illumination, and atmospheric …

Efforts have been made to detect both naturally occurring and anthropogenic changes to the
Earth's surface by using satellite remote sensing imagery. There is a need to maintain the …

Clouds in optical satellite images can be a source of information for water measurement or
viewed as contaminations that obstruct landscape observations. Thus, the use of a cloud …

With climate change, extreme storms are expected to occur more frequently. These storms
can cause severe forest damage, provoking direct and indirect economic losses for forestry …

Relative radiometric normalization (RRN) is a critical preprocessing step that is widely
applied to remote sensing data. Essential to RRN are the pseudo-invariant features (PIFs) …

Radiometric normalization is a fundamental and important preprocessing method for remote
sensing applications using multitemporal satellite images due to uncertainties of at-sensor …