List of Articles Hyperspectral Image

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  1 - Assessment of Performance Improvement in Hyperspectral Image Classification Based on Adaptive Expansion of Training Samples
  Maryam Imani
  10.7508/jist.2014.02.001
  High dimensional images in remote sensing applications allow us to analysis the surface of the earth with more details. A relevant problem for supervised classification of hyperspectral image is the limited availability of labeled training samples, since their collectio More

  High dimensional images in remote sensing applications allow us to analysis the surface of the earth with more details. A relevant problem for supervised classification of hyperspectral image is the limited availability of labeled training samples, since their collection is generally expensive, difficult and time consuming. In this paper, we propose an adaptive method for improving the classification of hyperspectral images through expansion of training samples size. The represented approach utilizes high-confidence labeled pixels as training samples to re-estimate classifier parameters. Semi-labeled samples are samples whose class labels are determined by GML classifier. Samples whose discriminator function values are large enough are selected in an adaptive process and considered as semi-labeled (pseudo-training) samples added to the training samples to train the classifier sequentially. The results of experiments show that proposed method can solve the limitation of training samples in hyperspectral images and improve the classification performance. Manuscript profile
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  2 - A Fast Algorithm for Hyperspectral Image Analysis Using SVM and Spatial Dependency
  H. Ghassemian Ahmad Keshavarz
  Recent significant development in sensor technology makes possible Earth observational remote sensing system with unprecedented spectral resolution and data dimensionality. The value of these new sensor systems lies in their ability to acquire a nearly complete optical More

  Recent significant development in sensor technology makes possible Earth observational remote sensing system with unprecedented spectral resolution and data dimensionality. The value of these new sensor systems lies in their ability to acquire a nearly complete optical spectrum for each pixel in the scene. Such imaging spectrometry now makes possible the acquisition of data in hundreds of spectral bands simultaneously, and it is called hyperspectral images. With the limited number of training samples of hyperspectral images, the classification of these images using conventional feature extraction algorithms (PCA, ICA, PP, DBFE, DAFE and Wavelet) is considered useless. In this paper a two stages classification algorithm is proposed, by fussing the spatial and spectral information. In the first stage the classes of each pixel and its eight neighbors are identified, using a classical classification algorithm. In the second stage two primary classes of a pixel and its neighbors are compared in each node of decision tree by a SVM. The proposed, binary tree SVM, takes advantage of both the efficient computation of the tree architecture and the high classification accuracy of SVM. The hyperspectral data set used in our experiments is a scene from Indiana’s Indian Pine by the AVIRIS sensor. The examples results show the problem of limited training samples can be mitigated using the proposed algorithm; moreover the computational time is significantly reduced. This suggests that binary tree SVM could be a promising tool for classifying hyperspectral images. Manuscript profile
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  3 - Variational Bayesian inference in Noise Removal from Hyperspectral Images Using Cluster-Based Latent Variables
  T. Bahraini Abass Ebrahimi moghadam M. Khademi H. Sadoghi Yazdi
  Removing noise from hyperspectral images is an inevitable step to improve the quality of these types of images. Many methods have been proposed by researchers in this field. Most of these methods do not address simultaneous spatial-spectral similarities. When the noise More

  Removing noise from hyperspectral images is an inevitable step to improve the quality of these types of images. Many methods have been proposed by researchers in this field. Most of these methods do not address simultaneous spatial-spectral similarities. When the noise removal method applies data globally without regard to spatial-spectral similarities, it usually has a negative effect on low-level pixels; when in the spectral data, a large number of pixels have little noise and a small number of pixels are destroyed by the high level of noise. In this paper, we first extract spatial-spectral similarities in images by defining cluster-based latent variables. In the following, a low-rank matrix factorization method based on these latent variables is proposed to eliminate the noise of hyperspectral images and to improve the resistance to noise (as compared to other methods). The performance of the proposed method is compared visually with six new methods on real noise-contaminated images. For quantitative comparison, the same experiments are done on clean images combined with six types of simulated noise. The simulation results show that by applying latent variables in the Bayesian inference framework, the performance of the noise removal method is improved and the proposed method performs better than the other methods. Manuscript profile