Deep learning (DL) is a powerful state-of-the-art technique for image processing including
remote sensing (RS) images. This letter describes a multilevel DL architecture that targets …

Satellite image time series, bolstered by their growing availability, are at the forefront of an
extensive effort towards automated Earth monitoring by international institutions. In …

Managing agricultural activity encompasses technology, geographic information, spatial
data and geomatic tools as support techniques. In this framework, agricultural map** is an …

Inspired by the recent success of deep convolutional neural networks (CNNs) and feature
aggregation in the field of computer vision and machine learning, we propose an effective …

Accurate classification and map** of crops is essential for supporting sustainable land
management. Such maps can be created based on satellite remote sensing; however, the …

Crop map** provides information on crop types and cropland spatial distribution.
Therefore, accurate and timely crop map** serves as the fundamental step to higher-level …

This paper presents a review of the conducted research in the field of multitemporal
classification methods used for the automatic identification of crops and arable land using …

In the paper we propose the methodology for solving the large scale classification and area
estimation problems in the remote sensing domain on the basis of deep learning paradigm …

The methodology for solving the problem of processing of large amount of remote sensing
data is proposed. The hierarchical structure of the model of deep learning method is based …

Due to differences in environmental factors, the phenology of the same crop is different
every year, causing divergent performances of the classifier built by spectral or time-series …