Governments pay agencies to control the activities of farmers who receive governmental
support. Field visits are costly and highly time-consuming; hence remote sensing is widely …

Accurate and efficient crop classification using remotely sensed data can provide
fundamental and important information for crop yield estimation. Existing crop classification …

In this article, we propose an approach based on Gaussian processes (GPs) for large-scale
land cover pixel-based classification with Sentinel-2 satellite image time series (SITS). We …

Satellite images with multispectral and panchromatic layers may contain a variety of
information about the ROI (region of interests). Each layer displays distinct regional …

Crop classification is a crucial task in agricultural remote sensing, with the accuracy of such
classification heavily relies on field sampling. Reusing historical samples can minimize the …

In this article, we propose a method exploiting irregular and unaligned Sentinel-2 satellite
image time series (SITS) for large-scale land cover pixel-based classification. We perform …

Temporal-based methods effectively improve the utilization rate of remote sensing images
but large ratios of missing information still need to be improved in the reconstruction models …

The classification of irregularly sampled Satellite image time-series (SITS) is investigated in
this paper. A multivariate Gaussian process mixture model is proposed to address the …

This paper presents an efficient model for analysing contextual geographic entities in
satellite data at a component level. The model utilizes various feature extraction techniques …

In this article, we propose an approach using irregular and unaligned Sentinel-2 satellite
image time series (SITS) for large-scale land cover pixel-based classification. We used …