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Hyperspectral Anomaly Detection based on Frequency analysis of Repeated Spatial Patterns

Hyperspectral Anomaly Detection based on Frequency Analysis ...

引用

Iranian Machine Vision and image processing (MVIP)

作者： Ashkan Taghipour Hassan Ghassemian Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728168326

ISBN: (纸本)9781728168333

Hyperspectral anomaly detection has brought significant attention to researchers during the last decade. In the remote sensing domain, it has lots of applications like environmental monitoring, mine source detection, human safety and, etc. Different methods have been proposed to overcome the challenges of anomaly detection, which mainly are a highly accurate detection rate and also having low false alarm rates. Background complexity of the hyperspectral data causes false detection of pixels as anomalies, and several statistical and sparse based methods try to solve it. However, the computation burden of the statistical and sparse based methods make them unpractical. Also, they often do not utilize the spatial information of hyperspectral data for increasing the detection accuracy. In this paper, we proposed a frequency analysis based approach that suppresses the repeatable patterns of the scene which mainly represent the background and considers the remainder regions as anomalies. Owe to the Fourier analysis advantage, it fast and accurate detects anomalous regions. Two well-known data sets have been chosen to challenge the potency of the proposed method, and the results are compared with some state-of-the-art methods. The visual and quantitative results confirm the supremacy of the proposed method to competitors in computation complexity and detection accuracy point of view.

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Sparse Representation of Injected Details for MRA-Based Pansharpening

Sparse Representation of Injected Details for MRA-Based Pans...

引用

Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Mehran Maneshi Hassan Ghassemian Maryam Imani Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728131146

ISBN: (纸本)9781728131153

Pansharpening is a notable remote sensing topic in which high spatial resolution panchromatic image and low spatial resolution multi-spectral image are being fused in order to receive the high spatial resolution multi-spectral image. This paper presents a hybrid pansharpening method based on MRA framework and the sparse representation of injected details. To add spatial details of the panchromatic image into the multispectral image more effectively, the injection gains are computed through an iterative full-scale model in which the gains are updated at each iteration relying on its previous iteration's fusion product. The proposed method is compared with five pansharpening approaches to investigate the effectiveness. Experiments have been implemented on two data sets from the Pleiades and GeoEye-1 satellites both at reduced and full scale. In terms of visual and quantity assessment, the high-resolution MS image produced by the proposed method is more acceptable than those images fused by other rival approaches.

关键词： Visualization Satellites Computational modeling Pansharpening Sensors Spatial resolution Remote sensing

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A Retina-Inspired Multiresolution analysis Framework for Pansharpening

A Retina-Inspired Multiresolution Analysis Framework for Pan...

引用

Iranian Machine Vision and image processing (MVIP)

作者： Mehran Maneshi Hassan Ghassemian Ghassem Khademi Maryam Imani Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728168326

ISBN: (纸本)9781728168333

Technical limitations on the satellite sensors make a trade-off between the spectral and spatial resolution in remotely sensed images. To deal with this issue, pansharpening has been emerged to prepare a single image with the high spatial and spectral resolution, simultaneously. This paper presents a pansharpening approach based on the retina-inspired model and the multiresolution analysis (MRA) framework. The retina- inspired model is simplified by the difference of Gaussian (DoG) operator, and we apply it to the panchromatic image to extract the spatial details. Furthermore, the injection gains in the MRA framework are calculated through an iterative process where the gains at each iteration are updated based on the fusion result obtained from its previous iteration. To investigate the performance of the proposed model, it is compared with some classical pansharpening approaches with two data sets captured by the GeoEye-1 and Pléiades satellite imagery sensors. The experimental results show the proposed retina-inspired pansharpening method acts well in injecting the spatial information along with reducing the spectral distortion.

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Unsupervised Hyperspectral Target Detection Using Spectral Residual of Deep Autoencoder Networks 4

Unsupervised Hyperspectral Target Detection Using Spectral R...

引用

4th International Conference on Pattern Recognition and image analysis, IPRIA 2019

作者： Taghipour, Ashkan Ghassemian, Hassan Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781728116211

Unsupervised hyperspectral target detection has attracted considerable attention in different applicable fields like environmental protection, agriculture monitoring, human rescue and etc. Due to the complexity of the background of hyperspectral images, accurate detection of target pixels using background estimation are faces lots of challenges which cause low accurate detection rates and high false alarm rates. In this paper we proposed an approach to estimate the background using a deep autoencoder neural networks architecture, then spectral residual error between the original hyperspectral image and the estimated one is considered for target detection using an exponential function. For evaluating the effectiveness and potency of the proposed method, several experiments are carried out on the well-known and challenging Hyperion dataset and the results are compared with some state-of-the-art methods. The visual and quantitative assessments show how spectral residual point of view using autoencoder networks can make high detection rates and low false alarms simultaneously. © 2019 IEEE.

关键词： Errors

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A Variational Pansharpening Algorithm Based on Total Variation and Primal-Dual Optimization 4

A Variational Pansharpening Algorithm Based on Total Variati...

引用

4th International Conference on Pattern Recognition and image analysis, IPRIA 2019

作者： Khademi, Ghassem Ghassemian, Hassan Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781728116211

This paper proposes a variational framework to estimate the high-resolution (HR) multispectral (MS) image from the low-resolution (LR) MS image and the panchromatic (Pan) image. The LR MS image is modeled as a decimation of the HR MS image. Furthermore, the Pan image is considered as a linear combination of the HR MS bands. A super-resolution (SR) model is defined in accordance with the image observation model and the total variation (TV) regularization. The SR reconstruction problem is modeled as a minimization problem, which is solved by an efficient primal-dual algorithm in a Euclidean setting. The result of comparing the proposed method with some recent classical and variational pansharpening methods proves the superiority of the proposed variational pansharpening algorithm. © 2019 IEEE.

关键词： Remote sensing

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Multispectral Pansharpening by Extracting Local Coefficients Based on WT Concept and MTF of Multispectral Sensors

Multispectral Pansharpening by Extracting Local Coefficients...

引用

Iranian Conference on Electrical Engineering (ICEE)

作者： Seyyedeh Zahra Azarnivar Hassan Ghassemian Science and Research Branch Islamic Azad University Tehran Iran Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

In order to increase the spatial resolution of the multispectral images, the fusion process is used that the high frequency spatial information of the panchromatic image is added to the multispectral image. In this paper, first, the panchromatic image of the histogram matching each multispectral image band and each multispectral image band interpolated to the panchromatic image scale was divided. A significant and soluble linear equation was obtained between the high-frequency spatial information of the multispectral image and the panchromatic image, and this model was applied to each local window. The scale invariance assumption was used to obtain the coefficients of fusion and for this purpose, the concept of wavelet transform was used, with its low pass filter frequency response being identical to the MTF multispectral image sensor. The proposed method was applied to two worldview2 and IKONOS datasets. The results were competitive with some popular fusion algorithms.

关键词： Spatial resolution Sensors image sensors Visualization Low-pass filters Nonlinear distortion Sensor fusion

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Correlation based Convolutional Recurrent Network for Load Forecasting

Correlation based Convolutional Recurrent Network for Load F...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Hosein Eskandari Maryam Imani Mohsen Parsa Moghadam Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

The safe and economical operation of a power grid is not possible without knowing the future load. For this reason, the first step in terms of productivity and proper management of a system will be to predict the electric load in the future. In this paper, the features that exist in the history of electric load consumption are examined and they are used as a guide for designing the proposed method. We try to predict short-term electric load by extracting the characteristics of the electric load history using deep neural networks. Long Short Term Memory (LSTM), are able to hold short and long-term memory for extracting relationships between the load values from time series. On the other hand, convolution neural networks are capable of automatically learning features and can directly generate a vector for prediction. The Correlation based Convolution Recurrent Network (CCRN) which is proposed in this paper calculate the autocorrelation coefficients of the load series and obtain an quasi-periodic of hourly loads with the highest correlations. Then, by using this quasi-periodic, the load series is converted into a two-dimensional matrix (image) and the two-dimensional convolutional neural networks is used to extract the load features. Finally, the load time sequence information is extracted using the LSTM network. Experimental results on both the Toronto and ISO-NE datasets show reduced prediction error as well as reduced training time compared with LSTM networks.

关键词： Correlation Training Neural networks Feature extraction Kernel Load forecasting Forecasting

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A Supervised Nonlinear Spectral Unmixing Method by Means of Neural Networks

A Supervised Nonlinear Spectral Unmixing Method by Means of ...

引用

International Conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Farshid Khajehrayeni Hassan Ghassemian Image Processing and Information Analysis Lab Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

Spectral unmixing (SU) technique is a conventional approach aiming at tackling the mixed pixel issue in hyperspectral imagery. In this study, we investigate the application of the convolutional networks (ConvNets) in SU. In fact, we propose two ConvNets, namely 1-D and 3-D in order to employ spectral and spectral-spatial information of the hyperspectral images, respectively. The proposed configurations are adapted by the VGG-Net that showed increasing depth with very small convolution filters (3×3) results in a substantial improvement. More importantly, rather than using the common mean squared error as the objective function, spectral information divergence is employed. Also, other network parameters, such as the number of kernels and kernel sizes are set according to the best findings in state-of-the-art methods in order to validate the network architecture. To demonstrate the superiority of the proposed ConvNets, some experiments are carried out on two well-known real hyperspectral data sets, namely Samson and Jasper Ridge, and the results are compared with some celebrated HSU approach in the literature. Moreover, the visual and quantitative assessments of the presented ConvNets display the necessity of incorporating spatial and spectral information in HSU.

关键词： Signal processing Intelligent systems

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Medical image Fusion based Sparse Decomposition and PDE of images

Medical Image Fusion based Sparse Decomposition and PDE of I...

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International Conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Fahim Shabanzade Hassan Ghassemian Mohammad Mahdi Sayadi Image Processing and Information Analysis Lab Faculty of Electrical and Computer Eng Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

Medical image fusion is used for increasing the quality of interpretation and diagnosis in medical applications. In this paper, a new fusion technique is presented. The method is based on Partial Differential Equation (PDE) and Sparse Decomposition (SD), to fuse MRI and PET images. The source images decomposed into low and high-frequency components and each component segmented into some overlapped patches. In the first step, a set of PDE filters is applied to the MRI image, which produces a multi-scale representation of MRI image. The filters only differ in smoothing characteristics, which are controlled by two distinct parameters. In the second step, it learns the multiple primary dictionaries by using the multi-scale representation of the MRI image which owns the main features of the image. Finally, it forms the dictionary from these primary dictionaries which obtained from the previous step and use it for highfrequency patch estimation. For low-frequency patches, we use a weighted averaging approach. The experimental results demonstrate the priority of the proposed method, in terms of both visual quality and quantitatively assessment.

关键词： Dictionaries Magnetic resonance imaging Transforms image fusion Filtering theory information filters

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Systolic Murmurs Diagnosis Improvement by Feature Fusion and Decision Fusion

Systolic Murmurs Diagnosis Improvement by Feature Fusion and...

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IEEE International Conference on Signal and image processing Applications (ICSIPA)

作者： Somayeh Akbari Hassan Ghassemian Zahra Akbari Image processing and Information Analysis Lab. Faculty of Electrical and Computer Eng. Tarbiat Modares University Tehran Iran

Acoustic sound generated by the heart mechanical activity, can provide useful information about the condition of heart valves. The heart sound auscultation is the fundamental tool in the evaluation of the cardiovascular system. The advantage of this method is fast, inexpensive and noninvasive. Due to human auscultatory limitation and non-stationary characteristics of phonocardiogram signals (PCG), diagnosis based on sounds that are heard via a stethoscope is difficult skill, therefor it requires a lot of practice. This study has proposed a biomedical automatic system for classification of PCG signals, which, recorded by a digital stethoscope. In order to extract various characteristics of PCG signals, the power spectrum estimation, wavelet transform (WT) and Mel frequency Cepstrum coefficients (MFCC) have been used in feature extraction step. Features are given to four classifiers: support vector machine (SVM), k-nearest neighbor (k-NN), multilayer perceptron (MLP) and maximum likelihood (ML). The majority voting combination rule is utilized for fusion of different classifiers. The proposed method has been examined on dataset of 90 PCG records containing healthy and three types of cardiac valve diseases (pulmonary stenosis (PS), Atrial Septal Defect (ASD) and Ventricular Septal Defect (VSD)). The experimental results demonstrate that the classifier fusion rule significantly increases the diagnostic accuracy of abnormal PCG. Our proposed method can be used for online classification of PCG in intelligent diagnosis systems.

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