Results 251 to 260 of about 35,627 (298)

Prediction accuracy of color imagery from hyperspectral imagery

SPIE Proceedings, 2005
In this paper we present the utilization of high-spectral resolution imagery for improving low-spectral resolution imagery. In our analysis, we assume that an acquisition of high spectral resolution images provides more accurate spectral predictions of low spectral resolution images than a direct acquisition of low spectral resolution images.
Peter Bajcsy, Rob Kooper
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Anomaly discrimination in hyperspectral imagery

SPIE Proceedings, 2014
Anomaly detection finds data samples whose signatures are spectrally distinct from their surrounding data samples. Unfortunately, it cannot discriminate the anomalies it detected one from another. In order to accomplish this task it requires a way of measuring spectral similarity such as spectral angle mapper (SAM) or spectral information divergence ...
Shih-Yu Chen, Drew Paylor, Chein-I Chang
openaire   +1 more source

Binary coding for hyperspectral imagery

SPIE Proceedings, 2004
Binary coding is one of simplest ways to characterize spectral features. One commonly used method is a binary coding-based image software system, called Spectral Analysis Manager (SPAM) for remotely sensed imagery developed by Mazer et al. For a given spectral signature, the SPAM calculates its spectral mean and inter-band spectral difference and ...
Jing Wang, Chein-I Chang, Chein-Chi Chang, Chinsu Lin   +3 more
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Spectral oversampling in hyperspectral imagery

SPIE Proceedings, 2003
This paper investigates if and how oversampling techniques can be applied in a useful manner to hyperspectral images. Oversampling occurs when the signal is sampled higher than the Nyquist frequency. If this occurs, the higher sampling rate can be traded for precision.
Shawn D. Hunt, Heidy Sierra
openaire   +1 more source

Compressed hyperspectral imagery for forestry

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2004
Various compression schemes have been suggested for storage and distribution of hyperspectral remotely sensed data. Hyperspectral forestry applications that rely on the measurement of subtle variations in the spectral signature of the forest canopy can be affected by modification of the spectra induced by compression.
A. Dyk, D.G. Goodenough, S. Thompson, C. Nadeau, A. Hollinger, null Shen-En Qian   +5 more
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Change detection for hyperspectral imagery

SPIE Proceedings, 2007
Change Detection (CD) is the process of identifying temporal or spectral changes in signals or images. Detection and analysis of change provide valuable information of transformations in a scene. Hyperspectral sensors provide spatial and spectrally rich information that can be exploited for Change Detection.
Karmon Vongsy, Shamsaddin Karimkhan, Arnab K. Shaw, Devert Wicker   +3 more
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Band Sampling for Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2022
Band sampling (BSam) is an innovative concept for hyperspectral imaging, which is derived from signal sampling in communications/signal processing as well as sampling theory in information theory. It is quite different from band selection (BSel) in several aspects.
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Unsupervised Unmixing of Hyperspectral Imagery

2006 49th IEEE International Midwest Symposium on Circuits and Systems, 2006
This paper presents an approach for simultaneous determination of end members and their abundances in hyperspectral imagery using a constrained positive matrix factorization. The algorithm presented here solves the constrained PMF using Gauss-Seidel method.
Yahya M. Masalmah, Miguel Velez-Reyes
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Unsupervised Detection in Hyperspectral Imagery

Optica Sensing Congress 2023 (AIS, FTS, HISE, Sensors, ES), 2023
Performance of six state-of-the-art unsupervised target detection algorithms were compared, including three least squares spectral unmixing based methods. UnConstrained Abundance, Nonnegatively Constrained Abundance and Fully Constrained Abundance performed best at detecting the desired endmembers.
Thomas Bahr, David Streutker, Greg Terrie, Michael Ingram   +3 more
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Central Attention Network for Hyperspectral Imagery Classification

IEEE Transactions on Neural Networks and Learning Systems, 2023
In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spatial information.
Huan Liu, Wei Li, Xiang-Gen Xia, Mengmeng Zhang, Chen-Zhong Gao, Ran Tao   +5 more
openaire   +2 more sources