Results 51 to 60 of about 11,320,128 (203)
Correlation Matrix-Based Fusion of Hyperspectral and Multispectral Images
Remote Sensing, 2023 The fusion of the hyperspectral image (HSI) and the multispectral image (MSI) is commonly employed to obtain a high spatial resolution hyperspectral image (HR-HSI); however, existing methods often involve complex feature extraction and optimization steps,
Hong Lin +5 more
doaj +1 more source
WHU-OHS: A benchmark dataset for large-scale Hersepctral Image classification
International Journal of Applied Earth Observations and Geoinformation, 2022 Hyperspectral image (HSI) classification is one of the most important remote sensing techniques. Currently, the performances of most of the HSI classification networks on the public HSI datasets are overoptimistic (i.e., the overall accuracy exceeds 98 %)
Jiayi Li, Xin Huang, Lilin Tu
doaj +1 more source
Effective feature extraction and data reduction with hyperspectral imaging in remote sensing [PDF]
, 2014 Although PCA has been widely used for feature extraction and data reduction, it suffers from three main drawbacks: high computational cost, large memory requirement and low efficacy in processing large datasets such as HSI.
Zabalza, Jaime +3 more
core +4 more sources
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Coastal wetlands mapping is a big challenge in remote sensing fields because of similar spectrum of different ground objects and their severe fragmentation and spatial heterogeneity.
Kai Liu +8 more
doaj +1 more source
Hyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion [PDF]
, 2011 Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels.
Avbelj, Janja +4 more
core
Hyperspectral Image Study based on Deep Learning Model HSI-ConvNext
Transactions on Computer Science and Intelligent Systems Research, 2023 In recent years, deep learning techniques have made significant research progress in the field of hyperspectral image processing. Hyperspectral images are widely used in agriculture, environmental monitoring, geological exploration and other fields due to their richness in material and spectral information about objects.
Zhonghui Bian +3 more
openaire +1 more source
Journal of Quaternary Science, EarlyView.Abstract Worldwide, lacustrine ecosystems are threatened by eutrophication and deoxygenation. Both processes are driven by the combined effects of human activities and climate change. Understanding the pre‐anthropogenic nexus between warming, eutrophication, deoxygenation, and related feedback is essential to determine how lakes respond to rapid ...
Noé R.M.M. Schmidhauser +7 more
wiley +1 more source
Journal of the Science of Food and Agriculture, EarlyView.Abstract Mycotoxins remain a persistent threat to the safety and quality of cereal grains and other agricultural products, and their impact on human health continues to raise global concerns. In many situations, the practices traditionally used to control these toxins are no longer sufficiently effective. They can be costly, difficult to implement on a
Abolfazl Asqardokht‐Aliabadi +2 more
wiley +1 more source
Cathodoluminescence hyperspectral imaging on the nanometre scale [PDF]
, 2011 Extending cathodoluminescence microscopy into the hyperspectral imaging mode brings significant benefits to an already powerful nano-scale characterization tool.
Martin, Robert +3 more
core +4 more sources
Effective classification of Chinese tea samples in hyperspectral imaging [PDF]
, 2013 Maximum likelihood and neural classifiers are two typical techniques in image classification. This paper investigates how to adapt these approaches to hyperspectral imaging for the classification of five kinds of Chinese tea samples, using visible light ...
Ren, Jinchang +2 more
core +4 more sources