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A Uav-based multisensor framework for legal industrial Cannabis monitoring and open-access dataset development. [PDF]

Data Brief
Rexha G, Papadhopulli I, Biberaj A, Agastra E, Sheme E, Meçe E.   +5 more
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Compression of hyperspectral imagery

Data Compression Conference, 2003. Proceedings. DCC 2003, 2003
High dimensional source vectors, such as those that occur in hyperspectral imagery, are partitioned into a number of subvectors of different length and then each subvector is vector quantized (VQ) individually with an appropriate codebook. A locally adaptive partitioning algorithm is introduced that performs comparably in this application to a more ...
Giovanni Motta, Francesco Rizzo, James A. Storer   +2 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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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.
Andrew Dyk, David G. Goodenough, Suzanne Thompson, Christian Nadeau, Allan Hollinger, Shen-En Qian   +5 more
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Modeling and detection in hyperspectral imagery

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
One aim of using hyperspectral imaging sensors is in discriminating man-made objects from dominant clutter environments. Sensors like Aviris or Hydice simultaneously collect hundreds of contiguous and narrowly spaced spectral band images for the same scene. The challenge lies in processing the corresponding large volume of data that is collected by the
Susan M. Schweizer, José M. F. Moura
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Fusion of Multispectral LiDAR and Hyperspectral Imagery

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
This paper presents a technique for the fusion of multispectral LiDAR and hyperspectral data. The proposed method is based on the fusion of the features of multispectral LiDAR and hyperspectral data projected in two different subspaces. First, the spatial features are extracted from both data using morphological filters.
Rasti, B., Ghamisi, P., Gloaguen, R.
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Interest segmentation of hyperspectral imagery

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010
In recent years, many new methods for analyzing spectral imagery have been introduced. These new methods have been developed to improve the analysis of hyperspectral imagery. Many of these techniques are data driven anomaly/target detection and spectral clustering algorithms which are used to decide whether a particular pixel or area is “interesting ...
Ariel Schlamm, David W. Messinger, William Basener   +2 more
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Restoration of hyperspectral imagery

SPIE Proceedings, 2006
In hyperspectral imaging, the quality of the collected spectral signatures can be degraded by blurring due to the channel weighting function of the imaging spectrometer. In this work, we are investigating reconstruction techniques to enhance salient features and remove degradation effects in measured spectra to assist in subsequent machine analysis.
Alejandra Umaña-Díaz, Miguel Vélez-Reyes   +1 more
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Adaptive coding of hyperspectral imagery

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
Two systems are presented for compression of hyperspectral imagery. These systems utilize adaptive classification, trellis-coded quantization, and optimal rate allocation. In the first system, DPCM is used for spectral decorrelation, while an adaptive wavelet-based coding scheme is used for spatial decorrelation.
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Dynamic band selection for hyperspectral imagery

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
This paper presents a new BS, called dynamic BS (DBS) which revolutionizes the commonly used BS by considering the number of bands to be selected, p as a variable which varies with criterion used for BS and different applications. Its idea is derived from information theory where it assumes that signal sources are considered as source alphabets with ...
Keng-Hao Liu, Chein-I Chang
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