Opin vísindi

Browsing by Subject "Hyperspectral imaging"

Browsing by Subject "Hyperspectral imaging"

Sort by: Order: Results:

  • Pálsson, Burkni; Sveinsson, Jóhannes Rúnar; Úlfarsson, Magnús Örn (2022-01-06)
    Deep learning has shown to be a powerful tool and has heavily impacted the data-intensive field of remote sensing. As a result, the number of published deep learning-based spectral unmixing techniques is proliferating. Blind hyperspectral unmixing (HU) ...
  • Benediktsson, Jon Atli; Ghamisi, Pedram; Couceiro, Micael S.; Fauvel, Mathieu (IEEE, 2014)
    A new spectral-spatial method for classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, fractional-order Darwinian particle swarm optimization and mean shift segmentation. The output of these ...
  • Benediktsson, Jon Atli; Ghamisi, Pedram; Martins, Fernando M. L.; Couceiro, Micael S. (IEEE, 2014)
    Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new ...
  • Rasti, Behnood; Scheunders, Paul; Ghamisi, Pedram; Licciardi, Giorgio; Chanussot, Jocelyn (MDPI AG, 2018-03-20)
    Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) ...
  • Ghamisi, Pedram; Ali, Abder-Rahman; Couceiro, Micael S.; Benediktsson, Jon Atli (IEEE, 2015)
    In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques that resemble ...
  • Benediktsson, Jon Atli; Ghamisi, Pedram; Couceiro, Micael S. (IEEE, 2015-05)
    A novel feature selection approach is proposed to address the curse of dimensionality and reduce the redundancy of hyperspectral data. The proposed approach is based on a new binary optimization method inspired by fractional-order Darwinian particle ...
  • Benediktsson, Jon Atli; Cavallaro, Gabriele; Riedel, Morris; Richerzhagen, Matthias; Plaza, Antonio (IEEE, 2015)
    Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the ...
  • Benediktsson, Jon Atli; Ulfarsson, Magnus; Ghamisi, Pedram (IEEE, 2014)
    Hyperspectral remote sensing technology allows one to acquire a sequence of possibly hundreds of contiguous spectral images from ultraviolet to infrared. Conventional spectral classifiers treat hyperspectral images as a list of spectral measurements ...
  • Kizel, Fadi; Shoshany, Maxim; Netanyahu, Nathan S.; Even-Tzur, Gilad; Benediktsson, Jon Atli (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    We present, in this paper, a new methodology for spectral unmixing, where a vector of fractions, corresponding to a set of endmembers (EMs), is estimated for each pixel in the image. The process first provides an initial estimate of the fraction vector, ...
  • Ghamisi, Pedram; Dalla Mura, Mauro; Benediktsson, Jon Atli (IEEE, 2015)
    Just over a decade has passed since the concept of morphological profile was defined for the analysis of remote sensing images. Since then, the morphological profile has largely proved to be a powerful tool able to model spatial information (e.g., ...