List of Articles Hyperspectral

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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 - Application of Curve Fitting in Hyperspectral Data Classification and Compression
  S. Abolfazl  Hosseini
  10.7508/jist.2015.03.002
  Regarding to the high between-band correlation and large volumes of hyperspectral data, feature reduction (either feature selection or extraction) is an important part of classification process for this data type. A variety of feature reduction methods have been develop More

  Regarding to the high between-band correlation and large volumes of hyperspectral data, feature reduction (either feature selection or extraction) is an important part of classification process for this data type. A variety of feature reduction methods have been developed using spectral and spatial domains. In this paper, a feature extracting technique is proposed based on rational function curve fitting. For each pixel of a hyperspectral image, a specific rational function approximation is developed to fit the spectral response curve of that pixel. Coefficients of the numerator and denominator polynomials of these functions are considered as new extracted features. This new technique is based on the fact that the sequence discipline - ordinance of reflectance coefficients in spectral response curve - contains some information which has not been considered by other statistical analysis based methods, such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) and their nonlinear versions. Also, we show that naturally different curves can be approximated by rational functions with equal form, but different amounts of coefficients. Maximum likelihood classification results demonstrate that the Rational Function Curve Fitting Feature Extraction (RFCF-FE) method provides better classification accuracies compared to competing feature extraction algorithms. The method, also, has the ability of lossy data compression. The original data can be reconstructed using the fitted curves. In addition, the proposed algorithm has the possibility to be applied to all pixels of image individually and simultaneously, unlike to PCA and other methods which need to know whole data for computing the transform matrix. Manuscript profile
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  3 - Optimized kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification
  Mohammad Hasheminejad
  10.52547/jist.16105.10.38.111
  Hyperspectral image (HSI) classification is an essential means of the analysis of remotely sensed images. Remote sensing of natural resources, astronomy, medicine, agriculture, food health, and many other applications are examples of possible applications of this techni More

  Hyperspectral image (HSI) classification is an essential means of the analysis of remotely sensed images. Remote sensing of natural resources, astronomy, medicine, agriculture, food health, and many other applications are examples of possible applications of this technique. Since hyperspectral images contain redundant measurements, it is crucial to identify a subset of efficient features for modeling the classes. Kernel-based methods are widely used in this field. In this paper, we introduce a new kernel-based method that defines Hyperplane more optimally than previous methods. The presence of noise data in many kernel-based HSI classification methods causes changes in boundary samples and, as a result, incorrect class hyperplane training. We propose the optimized kernel non-parametric weighted feature extraction for hyperspectral image classification. KNWFE is a kernel-based feature extraction method, which has promising results in classifying remotely-sensed image data. However, it does not take the closeness or distance of the data to the target classes. Solving the problem, we propose optimized KNWFE, which results in better classification performance. Our extensive experiments show that the proposed method improves the accuracy of HSI classification and is superior to the state-of-the-art HIS classifiers. Manuscript profile
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  4 - 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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  5 - Classification of Hyperspectral Images Using Cluster Space Linear Discriminant Analysis and Small Training Set
  M. Imani H. Ghassemian
  The hyperspectral images allow us to discriminate between different classes with more details. There are lots of spectral bands in hyperspectral images. On the other hand, the limited number of available training samples causes difficulties in classification of high dim More

  The hyperspectral images allow us to discriminate between different classes with more details. There are lots of spectral bands in hyperspectral images. On the other hand, the limited number of available training samples causes difficulties in classification of high dimensional data. Since the gathering of training samples is hard and time consuming, feature reduction can considerably improve the performance of classification. So, feature extraction is one of the most important preprocessing steps in analysis and classification of hyperspectral images. Feature extraction methods such as LDA have not good efficiency in small sample size situation. A supervised feature extraction method is proposed in this paper. The proposed method, which is called cluster space linear discriminant analysis (CSLDA), without obtaining the label of testing samples and just with doing a clustering on testing data, finds the relationship between training and testing samples. Then, it uses the power of unlabeled samples together with training samples for estimation of within-class and between-class scatter matrices. The CSLDA improves the classification accuracy particularly in multimodal hyperspectral data. The experimental results on urban and agriculture hyperspectral images show the better performance of CSLDA compared to popular feature extraction methods such as LDA, GDA, and NWFE using limited number of training samples. Manuscript profile
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  6 - 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
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  7 - Using hyperion hyperpectral data and field spectrometry for identification of hydrocarbon leakagesvia VISA-SCM combined methodology and spatial data mining
  Mohammad حمزه علی درویش بلورانی سید کاظم  علوی پناه فروغ  بیک حسین نصیری
  The hydrocarbon seepages theory puts forward a cause and effect relation ship between the oil and gas reservoir s and the specific surface anomalies which are basically related to hydrocarbon leakages as well as their related alterations. Hence,the s More

  The hydrocarbon seepages theory puts forward a cause and effect relation ship between the oil and gas reservoir s and the specific surface anomalies which are basically related to hydrocarbon leakages as well as their related alterations. Hence,the spectral reflectance of the hydrocarbons and their linked mineral alterntions produce credible pieces of evidence for oil and gas ex ploration .Hyperion images of EO-1 satellite was used in this study for identifying the oil seepages and their relevant alterations. After collecting the required data,the images under went the needed preprocessing. In order to recognize the oil seepages, these corrected data along with field-sampled spectrometric ones were employed. Then, VISA and SCM combined model was applied to indirectly identify the hydrocarbon seepages . Moreover, two hydrocarbon indexes were developed for direct recognition of the hydrocarbon seeps using Hyperion images. The finding indicate that the two mentioned techniques are efficacious for the purpose of the study at hand Manuscript profile