Volume 15 (2023)
Volume 14 (2022)
Volume 13 (2021)
Volume 12 (2020)
Volume 11 (2019)
Volume 10 (2018)
Volume 9 (2017)
Volume 8 (2016)
Volume 7 (2015)
Volume 6 (2014)
Volume 5 (2013)
Volume 4 (2012)
Volume 3 (2011)
Volume 2 (2010)
Volume 1 (2009)
Improving the Classification of Hyperspectral Images Using the Combined Model of CapsNet and the Extreme Gradient Boosting

Pouya Ahmadi; Tayebe Managhebi; Hamid Ebadi; Behnam Asghari

Volume 15, Issue 3 , September 2023, , Pages 41-60


  With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In ...  Read More

Improvement of Clustering for Hyperspectral Images using Spectral Information Divergence

Hamid Ezzatabadi Pour

Volume 10, Issue 3 , January 2019, , Pages 17-32

  K-Means is one of the most frequently used unsupervised classification approaches for remotely sensed image analysis. In standard K-Means version, the Euclidean distance (ED) has used to estimate the dissimilarity between an unknown vector data and the cluster center. Since, this measure is very sensitive ...  Read More

Improvement of Unsupervised Classification for Hyperspectral Images using Gustafson-Kessel Clustering Model

Hamid Ezzatabadi Pour; Saeid Homayouni

Volume 7, Issue 3 , November 2015, , Pages 97-114

  C-means clustering models are one of the most widely used methods for unsupervised classification of any data. Fuzzy c-means (FCM) is one of the most well-known clustering models in which, each data may be belonged to multiple clusters with different membership degree between 0 and 1. This model has ...  Read More