Recent developments in technology and data processing for Unoccupied Aerial Vehicles
(UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to …

Despite the proven significance of hyperspectral images (HSIs) in performing various
computer vision tasks, its potential is adversely affected by the low-resolution (LR) property …

Early drought stress detection is crucial for restoring productivity, ensuring recovery, and
providing vital information for mortality prevention. Hyperspectral remote sensing which is …

Hyperspectral anomaly detection (AD) aims to detect objects significantly different from their
surrounding background. Recently, many detectors based on autoencoder (AE) exhibited …

Traditional hyperspectral target detection methods use spectral domain information for target
recognition. Although it can effectively retain intrinsic characteristics of substances, targets in …

Highlights•DS-CNN works well for hyperspectral image strip noise removal.•DS-CNN can be
applied in real strip noise removal scenario.•DS-CNN can improve the performance of ND …

Machine Learning is beginning to provide state-of-the-art performance in a range of
environmental applications such as streamflow prediction in a hydrologic basin. However …

Characterizing the distribution, mechanism, and behaviour of invasive species is crucial to
implementing an effective plan to protect and manage native grassland ecosystems …

High-spatial-resolution images play an important role in land cover classification, and object-
based image analysis (OBIA) presents a good method of processing high-spatial-resolution …

Existing spectral reconstruction methods learn discrete mappings from spectrally
downsampled measurements (eg, RGB images) to a specific number of spectral bands …