MORPHOLOGICAL SEGMENTATION OF HYPERSPECTRAL IMAGES
Image Analysis & Stereology, 2011 The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on watershed, is composed of a spectral classification to obtain the markers and a vectorial gradient which gives the spatial information. Several alternative gradients are adapted
Noyel, Guillaume +2 more
openaire +7 more sources
Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model [PDF]
, 2012 This paper studies a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian
Altmann, Yoann +3 more
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Customizing kernel functions for SVM-based hyperspectral image classification [PDF]
, 2008 Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms.
Damper, R. I. +7 more
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In vivo hyperspectral imaging of skin malignant and benign tumors in visible spectrum
Journal of Biomedical Photonics & Engineering, 2018 The paper presents analysis of hyperspectral images for human skin cancer pathologies diagnostics. Hyperspectral images data contained backscattered spectra of normal skin and tumors. Analysis of hyperspectral images provided information about hemoglobin
Ivan A. Bratchenko +10 more
doaj +1 more source
TRANSFER LEARNING WITH LIMITED SAMPLES FOR THE SAME SOURCE HYPERSPECTRAL REMOTE SENSING IMAGES CLASSIFICATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 A classification method for hyperspectral datasets with a limited number of samples based on transferred convolutional neural network (CNN) is proposed. For the CNN model, a lot of labeled samples are needed for the classification of hyperspectral images,
W. Li, Q. Liu, Y. Wang, H. Li
doaj +1 more source
Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding [PDF]
, 2008 Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity.
Christophe, Emmanuel +2 more
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Target Detection System Design for Domestic Areas in Iran: Case Study in Abadan and Ahvaz, Using Satellite Multi-spectral Images of Landsat 8 and Sentinel 2 [PDF]
فناوری در مهندسی هوافضا, 2020 Hyperspectral images provide worthful spectral information for target detection. Since these images are not available in Iran, we use multi-spectral images with approximately 10 bands.
Maryam Imani
doaj
Smart Agricultural TechnologyThe accurate and effective monitoring of rice nitrogen status using hyperspectral datasets and estimation models is important for precision agriculture and intelligent breeding.
Zhonglin Wang +11 more
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Implementation strategies for hyperspectral unmixing using Bayesian source separation. [PDF]
, 2010 Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing. Moreover, it is sensible to impose a sum-to-one (full
Moussaoui, Saïd +11 more
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [PDF]
, 2012 Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras.
Paul Gader +13 more
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