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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...

引用

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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Spatial-Spectral Feature Extraction of Hyperspectral images using Attribute Profile With Partial Reconstruction and 3-D Gabor Filter Bank

Spatial-Spectral Feature Extraction of Hyperspectral Images ...

引用

International Conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Mohammad Dowlatshah Hassan Ghassemian Maryam Imani Image Processing and Data Analysis Lab Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

Simultaneously spatial-spectral feature extraction is preferred for classification of remotely sensed images. In this paper, attribute filters with partial reconstruction are applied for extraction of spatial characteristics of hyperspectral images. In addition, the 3-D Gabor filter bank is used for simultaneously extraction of spatial-spectral features. For implementation of attribute profiles with partial reconstruction, the first 3 principal components of data and 3 types of attributes (area, inertia, and standard deviation) are used for spatial features extraction. For extraction of 3-D Gabor features, firstly the 3-D Gabor filter bank is applied to the data. Then, the spatial and spectral properties in the first 20 principal components output are computed for classification. In the proposed method, the principal component analysis transform is used for dimensionality reduction and avoiding the complexity of the calculations. The support vector machine is used as classifier where its input is the spectral-spatial features extracted by attribute filters with partial reconstruction and 3-D Gabor filters.

关键词： Gabor filters Feature extraction Hyperspectral imaging image reconstruction Filter banks Standards

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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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A Model-Based Method for Pan-Sharpening of Multi-Spectral images using Sparse Representation

A Model-Based Method for Pan-Sharpening of Multi-Spectral Im...

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

作者： Mohammad Khateri Hassan Ghassemian Fardin Mirzapour Faculty of Electrical and Computer Engineering Image Processing and Information Analysis Lab. Tarbiat Modares University Tehran Iran Faculty of Electrical Engineering Sadra Institute of Higher Education Tehran Iran

Pan-sharpening (PS) is fusion of the low-resolution multi-spectral (LRM) image with the corresponding high resolution panchromatic (HRP) one, which aims to reach the high-resolution multi-spectral (HRM) image. Due to the importance of designing well-adapted dictionaries for the pansharpening problem as the fundamental challenge in the pan-sharpening methods utilizing sparse representation, we present a novel strategy to tackle this issue. Our method takes the image formation model into account in a patch-based strategy and exploits local spectral-spatial information. To make a fair tradeoff between spectral and spatial information of each patch, some local parameters are considered and estimated adaptively. In addition, the energy ratio between LRM/LRP patches is considered to locally reconstruct the same ratio for the corresponding HRM/HRP patches. This model-based pan-sharpening strategy led to a closed-form solution, which results in precise HRM dictionary atoms. To obtain the unknown HRM image, after designing the HRM dictionary from input Pan and multi-spectral images, sparse representation over LRM and HRM dictionaries will be conducted in the sparse coding stage. The proposed method has been applied to two different datasets collected by WorldView-3 and GeoEye-1, and then compared with some popular and state-of-the-art methods, qualitatively and quantitatively.

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A novel segmentation framework for uveal melanoma in magnetic resonance imaging based on class activation maps 2

A novel segmentation framework for uveal melanoma in magneti...

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2nd International Conference on Medical Imaging with Deep Learning, MIDL 2019

作者： Nguyen, Huu-Giao Pica, Alessia Hrbacek, Jan Weber, Damien C. La Rosa, Francesco Schalenbourg, Ann Sznitman, Raphael Cuadra, Meritxell Bach Proton Therapy Center Paul Scherrer Institut ETH Domain Villigen Switzerland Ophthalmic Technology Lab. ARTORG Center University of Bern Switzerland Switzerland Radiation Oncology Department Inselspital University of Bern Switzerland Signal Processing Lab. Ecole Polytechnique Fédérale de Lausanne Switzerland Adult Ocular Oncology Unit Jules-Gonin Eye Hospital Lausanne Switzerland Medical Image Analysis Laboratory CIBM University of Lausanne Switzerland

An automatic and accurate eye tumor segmentation from Magnetic Resonance images (MRI) could have a great clinical contribution for the purpose of diagnosis and treatment planning of intra-ocula cancer. For instance, the characterization of uveal melanoma (UM) tumors would allow the integration of 3D information for the radiotherapy and would also support further radiomics studies. In this work, we tackle two major challenges of UM segmentation: 1) the high heterogeneity of tumor characterization in respect to location, size and appearance and, 2) the difficulty in obtaining ground-truth delineations of medical experts for training. We propose a thorough segmentation pipeline consisting of a combination of two Convolutional Neural Networks (CNN). First, we consider the class activation maps (CAM) output from a Resnet classification model and the combination of Dense Conditional Random Field (CRF) with a prior information of sclera and lens from an Active Shape Model (ASM) to automatically extract the tumor location for all MRIs. Then, these immediate results will be inputted into a 2D-Unet CNN whereby using four encoder and decoder layers to produce the tumor segmentation. A clinical data set of 1.5T T1-w and T2-w images of 28 healthy eyes and 24 UM patients is used for validation. We show experimentally in two different MRI sequences that our weakly 2D-Unet approach outperforms previous state-of-the-art methods for tumor segmentation and that it achieves equivalent accuracy as when manual labels are used for training. These results are promising for further large-scale analysis and for introducing 3D ocular tumor information in the therapy planning. © 2019 H.-G. Nguyen, A. Pica, J. Hrbacek, D. Weber, F.L. Rosa, A. Schalenbourg, R. Sznitman & M. Bach Cuadra.

关键词： Magnetic resonance imaging

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