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检索条件"机构=Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering"

共 59 条记录，以下是21-30 订阅

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Hyperspectral Anomaly Detection based on Frequency analysis of Repeated Spatial Patterns 1

Hyperspectral Anomaly Detection based on Frequency Analysis ...

引用

1st International Conference on Machine Vision and image processing, MVIP 2020

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

ISBN: (纸本)9781728168326

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. © 2020 IEEE.

关键词： Anomaly detection

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A New Spectral-Spatial Network for Feature Fusion and Classification of Hyperspectral images

A New Spectral-Spatial Network for Feature Fusion and Classi...

引用

International Conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Mohamad Ebrahim Aghili Hassan Ghassemian Maryam Imani Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781665409391

Hyperspectral image (HSI) classification is one of the most important applications among all types of classification fields. Proper classification of spectral data leads to discovery of important land covers. In recent years, many methods have been introduced to increase the HSI classification accuracy. Methods based on neural networks show superior results compared to other methods. Among them, the two-dimensional convolutional neural networks (2D-CNNs) inspired by the human eye retina have achieved higher accuracy in classification. In most cases, HSI classifiers use only spectral features. In this paper, the spectral-spatial feature fusion and HSI classification using 2D-CNN are focused. For this purpose, the first 2D-convolutional layer of CNN is substituted by two combined 2D-Gabor-Shapelet filter banks. This layer extracts contextual information and provides valuable joint spectral-spatial features. The experimental results on real HSI (including the urban and agricultural areas and their mixture) show that the proposed method improves the overall classification performance. Compared to several famous HSI classification based on neural networks, the proposed method has higher speed and classification accuracy.

关键词： Training Neural networks Signal processing Feature extraction Retina Convolutional neural networks Gabor filters

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

Unsupervised Hyperspectral Target Detection Using Spectral R...

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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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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

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Spatial-spectral morphological feature extraction for hyperspectral images classification 5

Spatial-spectral morphological feature extraction for hypers...

引用

ISPRS International GeoSpatial Conference 2019, Joint Conferences of 5th Sensors and Models in Photogrammetry and Remote Sensing, SMPR 2019 and 3rd Geospatial information Research, GI Research 2019

作者： Dowlatshah, M. Ghassemian, H. Imani, M. Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Remote sensing image classification is a method for lab.ling pixels to show the Land cover types. The ambiguity in the classification process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the feature extraction process. One of the methods for spatial feature extraction is applying morphological filters. The basic idea of the morphological filters is comparison of structures within the image with a reference form called structural element. Four types of important morphological filters are included (dilation, erosion, opening, and closing) in this work. Opening morphological filter is used to extract spatial features where this filter is implemented by applying two successive sequences dilation and erosion operators. This filter removes the light areas smaller than the structural element in binary images;and in the gray level images, the areas smaller than the structural element and brighter than the neighboring regions are removed. Differential morphology filters are other important morphological filters, which are also used in this work. In the proposed method, the principal component analysis is used to reduce the data dimensions and an SVM classifier is applied to classify the hyperspectral data. The proposed method provides better classification results than the conventional morphological profile about 2%-5% for the University of Pavia and Pavia Center datasets. The results represent the good performance of the proposed method by using a small number of training samples. © 2019 M. Dowlatshah et al.

关键词： Feature extraction

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A critical review of quality assessment protocols in pan-sharpening 5

A critical review of quality assessment protocols in pan-sha...

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ISPRS International GeoSpatial Conference 2019, Joint Conferences of 5th Sensors and Models in Photogrammetry and Remote Sensing, SMPR 2019 and 3rd Geospatial information Research, GI Research 2019

作者： Aghapour Maleki, S. Ghassemian, H. Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Although vast amounts of pan-sharpening methods have been proposed to date, there has been relatively little published on the topic of qualitative and quantitative assessment of the pan-sharpened multispectral (MS) data. Since a high resolution reference MS image is not availab.e, qualitative and quantitative assessments of the spatially enhanced MS image are a much more challenging task in pan-sharpening. Thomas et al. conducted a critical survey of conventional pan-sharpening methods considering remote sensing physics. In this paper, we study the effects of physical constraints of satellite sensors on conventional quality assessment protocols. The novelty of this work is in investigating the effect of physical constraints on the performance of quality assessment protocols. The most popular protocols, which are analysed here, are quality not requiring a reference (QNR), Wald's protocol, Zhou's protocol, Khan's protocol and Ghassemi's protocol. In doing so, the strengths and weaknesses of such protocols regarding the physical limitations on satellite sensors are discussed. © 2019 S. Aghapour Maleki.

关键词： image fusion

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Local binary graph feature reduction for three-dimensional gabor filter based hyperspectral image classification 5

Local binary graph feature reduction for three-dimensional g...

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ISPRS International GeoSpatial Conference 2019, Joint Conferences of 5th Sensors and Models in Photogrammetry and Remote Sensing, SMPR 2019 and 3rd Geospatial information Research, GI Research 2019

作者： Darvishnezhad, M. Ghassemian, H. Imani, M. Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

One of the challenges of the hyperspectral image classification is the fusing spectral and spatial features. There are several methods for fusing features in hyperspectral image classification. Three-Dimensional Gabor Filters are the best method to extract spectral and spatial features simultaneously. However, one of the problems with using the 3D Gabor filter is the high number of extracted features. In this paper, to reducing extracted features from 3D-Gabor filters and increasing the classification accuracy in hyperspectral images, a novel method named Local Binary Graph (LBG) is used. The LBG method uses a local graph to solve the optimization problem, which maps each pixel to the reduced dimension image and improves the McNemar test result in comparison with the existing methods. Finally, the result of the proposed method achieved 96.2% and 92.6% overall accuracy for Pavia University and Indian Pines data set, respectively. © 2019 M. Darvishnezhad et al.

关键词： Gabor filters

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