29,397 research outputs found
A ROBUST SUPERVISED METHOD TO ESTIMATE CHLOROPHYLL AB CONTENT FROM SPECTRAL REFLECTANCE.
No full textThe research presented in this paper is funded by the Research Foundation-Flanders -project G031921N
An efficient method for water content estimation of building materials from spectral reflectance
No full textThe research presented in this paper is funded by the Research Foundation-Flanders, Belgium-project G031921N. Bikram Koirala is a postdoctoral fellow of the Research Foundation Flanders (FWO: 1250824N-7028) . The funding sources were not involved in data col-lection, analysis, or interpretation; or any aspect pertinent to the study
Shadow-aware nonlinear spectral unmixing with spatial regularization
No full textCurrent shadow-aware hyperspectral unmixing (HySU) methods often suffer from noisy abundance maps and inaccurate abundance estimation of shadowed pixels, as these are characterized by low reflectance values and signal-to-noise ratio. In order to achieve a shadow-insensitive abundance estimation, in this article, we propose a novel spatial–spectral shadow-aware mixing (S3AM) model. The approach models shadows by considering diffuse solar illumination and secondary illumination from neighboring pixels. Besides, spatial regularization using shadow-aware weighted total variation (TV) is employed. Specifically, pixels in the local neighborhood of a target pixel simultaneously consider spectral similarity measures derived from the imagery, elevation similarity measures derived from a digital surface model (DSM), and the impact of shadows. The sky view factor F , needed as input for the model, is also derived from available DSMs. The proposed approach is extensively validated and compared with state-of-the-art methods on two datasets. Results demonstrate that the S3AM yields superior abundance estimation maps for real scenarios, by decreasing the noise in the results and achieving more accurate reconstructions in the presence of shadows
Hyperspectral and multispectral image fusion based on spectral matching in the shearlet domain
No full textEstimation of the number of endmembers in a hyperspectral image via the Hubness phenomenon
No full textUnsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm
No full textThis work was supported by the Federal Ministry of Education and Research (BMBF), and in the Client II Program, within theMoCa project under Grant 033R189B. The work of Behnood Rasti was funded by Alexander von Humboldt foundation. (Corresponding author: Kasra Rafiezadeh Shahi.
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