Results 11 to 20 of about 6,103 (201)
Spectral Unmixing via Data-Guided Sparsity [PDF]
IEEE Transactions on Image Processing, 2014 Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization and understanding. From an unsupervised learning perspective, this problem is very challenging---both the spectral bases and their composite ...
Zhu, Feiyun +5 more
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USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 The best technique to extract information from remotely sensed image is classification. The problem of traditional classification methods is that each pixel is assigned to a single class by presuming all pixels within the image.
P. Javadi
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A Global Spectral–Spatial Feature Learning Network for Semisupervised Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Neural networks have been widely applied in hyperspectral unmixing in the past few years. However, most networks only focus on extracting the spectral information or local spectral–spatial correlation of a single pixel. In order to further explore
Fanqiang Kong +3 more
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HYPERSPECTRAL IMAGE RESOLUTION ENHANCEMENT BASED ON SPECTRAL UNMIXING AND INFORMATION FUSION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 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. Spectral unmixing methods can be
J. Bieniarz +4 more
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Context Dependent Spectral Unmixing
2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012 A hyperspectral unmixing algorithm that finds multiple sets of endmembers is introduced. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel objective function that combines context identification and unmixing into a joint ...
Hamdi Jenzri, Hichem Frigui, Paul Gader
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A blind spectral unmixing in wavelet domain [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral data - Systems, Processing, Information ...
Vijayashekhar S S, Jignesh S. Bhatt
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Frontiers in Signal Processing, 2022 Recently, the development of learning-based algorithms has shown a crucial role to extract features of vital importance from multi-spectral photoacoustic imaging.
Valeria Grasso +6 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 The autoencoder (AE) framework is usually adopted as a baseline network for hyperspectral unmixing. Totally an AE performs well in hyperspectral unmixing through automatically learning low-dimensional embedding and reconstructing data.
Xiao Chen +5 more
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Spectral Unmixing of Pigments on Surface of Painted Artefacts Considering Spectral Variability [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesPainted artefacts, such as murals and paintings, are the treasures of human civilization. Pigment is an important component of their surfaces. It is crucial to study the composition and proportion of pigments on the surface of painted artefacts for the ...
Y. Wang +10 more
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Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation. With the help of large spectral libraries, unmixing is equivalent to finding the optimal subset of the library entries that can best model the image.
Fan Li +5 more
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