In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …

In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Developing predictive models plays an important role …

Abstract In recent years, Deep Learning (DL), such as the algorithms of Convolutional
Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial …

Building objects is one of the principal features that are essential for updating the geospatial
database. Extracting building features from high-resolution imagery automatically and …

Statement of Retraction We, the Editor and Publisher of the journal European Journal of
Remote Sensing, have retracted the following articles that were published in the Special …

In this paper, we present tensor-based linear and nonlinear models for hyperspectral data
classification and analysis. By exploiting the principles of tensor algebra, we introduce new …

Coastal mangrove forests provide important ecosystem goods and services, including
carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are …

We are currently living in the era of big data. The volume of collected or archived geospatial
data for land use and land cover (LULC) mapping including remotely sensed satellite …

Information on forest composition, specifically tree types and their distribution, aids in timber
stock calculation and can help to better understand the biodiversity in a particular region …

Lossy compression of remote sensing data has found numerous applications. Several
requirements are usually imposed on methods and algorithms to be used. A large …