Results 11 to 20 of about 6,533 (191)
Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing
Remote Sensing, 2021 Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult ...
Ziqiang Hua +3 more
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Spectral Unmixing With Multiple Dictionaries [PDF]
IEEE Geoscience and Remote Sensing Letters, 2018 Spectral unmixing aims at recovering the spectral signatures of materials, called endmembers, mixed in a hyperspectral or multispectral image, along with their abundances. A typical assumption is that the image contains one pure pixel per endmember, in which case spectral unmixing reduces to identifying these pixels.
Jeremy E. Cohen, Nicolas Gillis
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 With the support of spectral libraries, sparse unmixing techniques have gradually developed. However, some existing sparse unmixing algorithms suffer from problems, such as insufficient utilization of spatial information and sensitivity to noise.
Yao Liang +4 more
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Applied Sciences, 2022 The purpose of hyperspectral unmixing (HU) is to obtain the spectral features of materials (endmembers) and their proportion (abundance) in a hyperspectral image (HSI).
Baohua Jin +4 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 +2 more
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On the application of spectral unmixing for noise reduction [PDF]
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013 Spectral unmixing aims at decomposing each image element of a hyperspectral scene in signals typically related to pure materials. This paper gives an added value to the results of this process by proposing Unmixing-based Denoising (UBD), a supervised methodology to recover bands characterized by a low Signal-to-Noise Ratio in a hyperspectral scene.
Cerra, Daniele +3 more
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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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