Results 121 to 130 of about 15,264 (300)

Triplet constrained deep feature extraction for hyperspectral image classification

, 2018
Convolutional neural networks (CNNs) have demonstrated significant performance in various visual recognition problems in recent years. Recent research has shown that training multilayer neural networks can extensively improve the performance of ...
Zhou, Jun, Alam, Fahim, Jo, Jun, Liew, Wee Chung, Gao, Yongsheng   +4 more
core   +1 more source

Using phenology to improve invasive plant detection in fine‐scale hyperspectral drone‐based images

Remote Sensing in Ecology and Conservation, EarlyView.
Using drone‐based hyperspectral images of mixed temperate successional forests collected over a growing season, detection algorithms were produced for three invasive species of interest, which are not only invasive in Virginia but also much of the U.S.: Ailanthus altissima (tree of heaven), Elaeagnus umbellata (autumn olive), and Rhamnus davurica ...
Kelsey S. Huelsman, Howard E. Epstein, Xi Yang, Roderick Walker   +3 more
wiley   +1 more source

HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis

Remote Sensing
The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective.
Daniel La’ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona, Oscar Mendez   +3 more
doaj   +1 more source

Classification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA

, 2020
This paper is based on an experimentation to preserve shapes of the natural classes in a hyperspectral image post classification of the image using SVM. The classifier classifies the vegetation types present in the hyperspectral image and then estimates ...
Aditi Chandra, Narayan Panigrahi
core  

Spatial analysis for colon biopsy classification from hyperspectral imagery [PDF]

, 2008
Automatic classification of histology images, the objective of our research, is aimed at supporting the pathologists in their diagnosis. In this paper, we present a comparative study between 3D spectral/spatial analysis (SSA) and 2D spatial analysis (SA ...
Rajpoot, Nasir M. (Nasir Mahmood), Masood, Khalid   +1 more
core  

Modelling forest dynamics using integral projection models and repeat lidar

Remote Sensing in Ecology and Conservation, EarlyView.
Forests are facing increasing pressure from climate change and disturbance, yet linking individual tree trajectories to whole‐forest outcomes remains a major challenge. Our study integrates repeat airborne lidar with an Integral Projection Model to analyse demographic processes at the landscape scale.
Alice Rosen, Robin Battison, Christina M. Hernández, Oliver G. Spacey, Jessica McLean, Suzanne M. Prober, Samuel J. L. Gascoigne, Sean McMahon, Tommaso Jucker, Roberto Salguero‐Gómez   +9 more
wiley   +1 more source

A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation

, 2016
Traditional hyperspectral image classification algorithms focus on spectral' information application, however, with the increase of spatial resolution of hyperspectral remote sensing images, hyperspectral imaging presents clustering properties on ...
Wang, HW, Wen, J, Hu Bing-liang, Xu, J, Wen Jia, Li Xiang-juan, Wang, CL, Hu, BL, Xu Jun, Wang Hong-wei, Wang Cai-ling, Li, XJ   +11 more
core   +1 more source

Hyperspectral species maps and LiDAR‐based structured population models show future forest fire frequency may compromise forest resilience

Remote Sensing in Ecology and Conservation, EarlyView.
Monitoring forest recovery from disturbances at scale requires tracking tree dynamics, yet traditional ground‐based approaches are resource‐intensive. We present a pipeline to parameterize integral projection models (IPMs) using LiDAR data and hyperspectral‐based species maps to assess post‐fire recovery across large, forested areas at the Caribou ...
Jessica McLean, Tommaso Jucker, Alice Rosen, Sean M. McMahon, Roberto Salguero‐Gómez   +4 more
wiley   +1 more source

Scale dependence in remotely sensed biodiversity: Leveraging continental‐scale imaging spectroscopy from the National Ecological Observatory Network

Remote Sensing in Ecology and Conservation, EarlyView.
Imaging spectroscopy enables large‐scale biodiversity assessment, yet spectral diversity metrics are scale dependent. Across 15 NEON ecosystems, we find that spectral richness increases sub‐linearly from 3600 m2 to 4 km2, whereas spectral divergence shows weak or inconsistent scaling with area, underscoring the importance of scale‐aware interpretation ...
Meghan T. Hayden, Matthew W. Rossi, Laura E. Dee, Kyle Kovach, Cibele H. Amaral, Jacob Nesslage, Madeline Slimp, Rachel S. Meyer, E. Natasha Stavros   +8 more
wiley   +1 more source

Monitoring of Crustose Coralline Algae Using Low‐Altitude Unmanned Aerial Vehicles in Intertidal Reefs

Remote Sensing in Ecology and Conservation, EarlyView.
High‐resolution visible‐light imagery from low‐altitude unmanned aerial vehicles, combined with superpixel segmentation and a Random Forest classifier, provides an efficient and scalable framework for mapping and monitoring crustose coralline algae and reef habitats.
Po‐Chien Lin, Han‐Yang Yeh, Zhi‐Cheng Huang   +2 more
wiley   +1 more source