Many Hyperspectral imagery applications require a response in real time or near-real time. To meet this requirement this paper proposes a parallel unmixing method developed for graphics processing units (GPU). This method is based on the vertex component analysis (VCA), which is a geometrical based method highly parallelizable. VCA is a very fast and accurate method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Experimental results obtained for simulated and real hyperspectral datasets reveal considerable acceleration factors, up to 24 times
Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture anal...
Hyperspectral imaging can be used for object detection and for discriminating between different obje...
We present a new algorithm for feature extraction in hyperspectral images based on source separation...
Many Hyperspectral imagery applications require a response in real time or near-real time. To meet t...
Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other m...
This letter presents a new parallel method for hyperspectral unmixing composed by the efficient comb...
International Conference with Peer Review 2012 IEEE International Conference in Geoscience and Remot...
In this paper, we present a new parallel implementation of the Vertex Component Analysis (VCA) algor...
[[abstract]]Hyperspectral images can be used to identify the unique materials present in an area.Due...
Hyperspectral images are used in different applications in Earth and space science, and many of thes...
Hyperspectral sensors are being developed for remote sensing applications. These sensors produce hug...
One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the l...
This paper proposes an FPGA-based architecture for onboard hyperspectral unmixing. This method based...
Hyperspectral imaging has become one of the main topics in remote sensing applications, which compri...
Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is de...
Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture anal...
Hyperspectral imaging can be used for object detection and for discriminating between different obje...
We present a new algorithm for feature extraction in hyperspectral images based on source separation...
Many Hyperspectral imagery applications require a response in real time or near-real time. To meet t...
Endmember extraction (EE) is a fundamental and crucial task in hyperspectral unmixing. Among other m...
This letter presents a new parallel method for hyperspectral unmixing composed by the efficient comb...
International Conference with Peer Review 2012 IEEE International Conference in Geoscience and Remot...
In this paper, we present a new parallel implementation of the Vertex Component Analysis (VCA) algor...
[[abstract]]Hyperspectral images can be used to identify the unique materials present in an area.Due...
Hyperspectral images are used in different applications in Earth and space science, and many of thes...
Hyperspectral sensors are being developed for remote sensing applications. These sensors produce hug...
One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the l...
This paper proposes an FPGA-based architecture for onboard hyperspectral unmixing. This method based...
Hyperspectral imaging has become one of the main topics in remote sensing applications, which compri...
Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is de...
Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture anal...
Hyperspectral imaging can be used for object detection and for discriminating between different obje...
We present a new algorithm for feature extraction in hyperspectral images based on source separation...