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 …

Hyperspectral imaging (HSI) integrates conventional imaging and spectroscopy, to obtain
both spatial and spectral information from a specimen. This technique enables investigators …

Convolutional neural networks (CNNs) are widely used in hyperspectral image (HSI)
classification. However, the fixed receptive field of CNN-based methods limits their capability …

Supervised machine learning and deep learning methods perform well in hyperspectral
image classification. However, hyperspectral images have few labeled samples, which …

Hyperspectral data (Table 1) is acquired as continuous narrowbands (eg, each band with 1
to 10 nanometer or nm bandwidths) over a range of electromagnetic spectrum (eg, 400 …

To adopt sustainable crop practices in changing climate, understanding the climatic
parameters and water requirements with vegetation is crucial on a spatiotemporal scale. The …

Information on land use and land cover (LULC) including forest cover is important for the
development of strategies for land planning and management. Satellite remotely sensed …

Accurately map** the spatial distribution of tree species in tropical environments provides
valuable insights for ecologists and forest managers. This process may play an important …

Satellite data and aerial photos have proved to be useful in efficient conservation and
management of mangrove ecosystems. However, there have been only very few attempts to …

Recently, collaborative representation classification (CRC) has attracted much attention for
hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved …