Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Normalized difference vegetation index (NDVI) derived from satellites has been ubiquitously
utilized in the field of remote sensing. Nevertheless, there are multitudinous contaminations …

In recent years, deep-learning techniques revolutionized the way remote sensing data are
processed. The classification of hyperspectral data is no exception to the rule, but it has …

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are
available for land-cover mapping. However, due to the complex information brought by the …

Deep-learning (DL) algorithms, which learn the representative and discriminative features in
a hierarchical manner from the data, have recently become a hotspot in the machine …

We propose an end-to-end framework for the dense, pixelwise classification of satellite
imagery with convolutional neural networks (CNNs). In our framework, CNNs are directly …

In this paper, we propose a spectral–spatial feature based classification (SSFC) framework
that jointly uses dimension reduction and deep learning techniques for spectral and spatial …

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …

Remote sensing (RS) image classification plays an important role in the earth observation
technology using RS data, having been widely exploited in both military and civil fields …