Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Sousa, Daniel P."'
Autor:
Small, Christopher, Sousa, Daniel
Due to their transient nature, clouds represent anomalies relative to the underlying landscape of interest. Hence, the challenge of cloud identification can be considered a specific case in the more general problem of anomaly detection. The confoundi
Externí odkaz:
http://arxiv.org/abs/2401.06225
Autor:
Small, Christopher, Sousa, Daniel
Characterization of topology and dimensionality of spectral feature spaces provides insight into information content. The objective of this study is to characterize topology and spectral dimensionality of spectral mixing spaces representing a diversi
Externí odkaz:
http://arxiv.org/abs/2307.04716
Autor:
Sousa, Daniel, Small, Christopher
NASA's Earth Surface Mineral Dust Source Investigation (EMIT) mission seeks to use spaceborne imaging spectroscopy (hyperspectral imaging) to map the mineralogy of arid dust source regions. Here we apply recent developments in Joint Characterization
Externí odkaz:
http://arxiv.org/abs/2303.04876
Autor:
Sousa, Daniel, Small, Christopher
For decades, agronomists have used remote sensing to monitor key crop parameters like biomass, fractional cover, and plant health. Vegetation indices (VIs) are popular for this purpose, primarily leveraging the spectral red edge in multispectral imag
Externí odkaz:
http://arxiv.org/abs/2208.06480
Autor:
Small, Christopher, Sousa, Daniel
Hyperspectral feature spaces are useful for many remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In such cases, characterization of the feature space dimensionality, geometry and topology can pr
Externí odkaz:
http://arxiv.org/abs/2112.01416
Autor:
Sousa, Daniel, Small, Christopher
Spatiotemporal (ST) image data are increasingly common and often high-dimensional (high-D). Modeling ST data can be a challenge due to the plethora of independent and interacting processes which may or may not contribute to the measurements. Characte
Externí odkaz:
http://arxiv.org/abs/2108.09545
Autor:
Sousa, Daniel, Small, Christopher
Publikováno v:
Advances in Artificial Intelligence and Machine Learning, 1 (3):203-220 (2021)
High dimensional data can contain multiple scales of variance. Analysis tools that preferentially operate at one scale can be ineffective at capturing all the information present in this cross-scale complexity. We propose a multiscale joint character
Externí odkaz:
http://arxiv.org/abs/2102.09669
Patch prioritization is a crucial aspect of information systems security, and knowledge of which vulnerabilities were exploited in the wild is a powerful tool to help systems administrators accomplish this task. The analysis of social media for this
Externí odkaz:
http://arxiv.org/abs/2011.03113
Autor:
Dutta, Ritabrata, Zouaoui-Boudjeltia, Karim, Kotsalos, Christos, Rousseau, Alexandre, de Sousa, Daniel Ribeiro, Desmet, Jean-Marc, Van Meerhaeghe, Alain, Mira, Antonietta, Chopard, Bastien
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation o
Externí odkaz:
http://arxiv.org/abs/2010.06465
Zipfs Law states that rank-size distributions of city populations follow a power law with an exponent of -1. The assertion of a universal power law is controversial because the linearity and slope appear to vary over time and among countries. We comp
Externí odkaz:
http://arxiv.org/abs/2004.14237