In recent years, the rapid development of Earth observation technology has produced an
increasing growth in remote sensing big data, posing serious challenges for effective and …

Recent research shows that deep-learning-derived methods based on a deep convolutional
neural network have high accuracy when applied to hyperspectral image (HSI) …

The study presented in this paper introduced a new fully automatic three-dimensional
building reconstruction method that can generate first level of detail (LoD 1) building models …

Urban land cover and land use mapping plays an important role in urban planning and
management. In this paper, novel multi-scale deep learning models, namely ASPP-Unet and …

Various research applications require detailed metrics to describe the form and composition
of cities at fine scales, but the parameter computation remains a challenge due to limited …

Enhancing the spatial resolution of hyperspectral image (HSI) is of significance for
applications. Fusing HSI with a high resolution (HR) multispectral image (MSI) is an …

Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use
of optical remote sensing for Earth monitoring applications. A possible alternative is to …

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 …

Efficient and accurate semantic segmentation is the key technique for automatic remote
sensing image analysis. While there have been many segmentation methods based on …

Extracting buildings from very high resolution (VHR) images has attracted much attention but
is still challenging due to their large varieties in appearance and scale. Convolutional neural …