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7th International Conference on Signal processing and Intelligent Systems, ICSPIS 2021

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

ISBN: (纸本)9781665409384

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. © 2021 IEEE.

关键词： Gabor filters

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A New Composite Multimodality image Fusion Method Based on Shearlet Transform and Retina Inspired Model 1

A New Composite Multimodality Image Fusion Method Based on S...

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

作者： Sayadi, Mohammadmahdi Ghassemian, Hassan Naimi, Reza Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab. Tehran Iran

ISBN: (纸本)9781728168326

Medical imaging is a very important element in disease diagnosis. MRI image has structural information, while PET image has functional information. However, there is no medical imagery device that has both structural and functional information simultaneously. Thus, the image fusion technique is used. This work concentrates on PET and MRI fusion. It is based on the combination of retina-inspired model and Non-Subsampled shearlet transform. In the first step, the high-frequency component is obtained by applying the shearlet transform to the MRI image, which produces sub-images in several scales and directions, and by adding up these images together a single edge image is reconstructed. In the second step, the PET image is transferred from RGB color space into IHS color space. Then the low-frequency component is produced by applying a Gaussian low pass filter to the luminance channel of the PET image. By adding up low frequency component and high-frequency component together and transferring the result from IHS color space to RGB color space the fused image is obtained. © 2020 IEEE.

关键词： image fusion

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Estimation of Free Parameters of Morphological Profiles for Building Extraction Using SAR images

Estimation of Free Parameters of Morphological Profiles for ...

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International Conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Fateme Amjadipour Hassan Ghassemian Maryam Imani Image Processing and Information Analysis Lab Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781665409391

Nowadays, one of the most challenging issues in the field of remote sensing and satellite imagery is production of cadastral maps. Synthetic aperture radar (SAR) images are among the most widely used satellite images in the last decade. Due to radar nature of these images, the buildings in SAR images face with two problems: shadow and layover. Morphological mathematics are efficient tools for detection of buildings with providing a contextual profile containing shape and geometrical characteristics of the objects in radar images. By using the suggested method, two characteristics of shadow and brightness are detected separately. Then, the construction areas are extracted by using a fuzzy fusion approach. In this method, various parameters such as size and direction of the structural element and the weighting factor of the shadow, bright area, and the recursive parameter have to be determined independently. To this end, an iterative method using MSE is suggested. The experimental results show a detection rate of 94.3% achieved by the proposed method.

关键词： Satellites Shape Spaceborne radar Roads Buildings Radar imaging Signal processing

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Deep Curriculum Learning for PolSAR image Classification

arXiv

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arXiv 2021年

作者： Mousavi, Hamidreza Imani, Maryam Ghassemian, Hassan Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetric synthetic aperture radar (PolSAR) data. This method utilizes the entropy-alpha target decomposition method to estimate the degree of complexity of each PolSAR image patch before applying it to the convolutional neural network (CNN). Also, an accumulative mini-batch pacing function is used to introduce more difficult patches to CNN. Experiments on the widely used data set of AIRSAR Flevoland reveal that the proposed curriculum learning method can not only increase classification accuracy but also lead to faster training convergence. © 2021, CC BY.

关键词： Curricula

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A Retina-Inspired Multiresolution analysis Framework for Pansharpening 1

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

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

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

ISBN: (纸本)9781728168326

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. © 2020 IEEE.

关键词： Remote sensing

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

Hyperspectral Anomaly Detection based on Frequency Analysis ...

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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...

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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

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

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Topic Embedded Representation Enhanced Variational Wasserstein Autoencoder for Text Modeling 5

Topic Embedded Representation Enhanced Variational Wasserste...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Xiang, Zheng Liu, Xiaoming Yang, Guan Liu, Yang Zhongyuan University of Technology School of Computer Science Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Key Laboratory of text processing and image understanding Zhengzhou China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China Shandong University Key Lab of Cryptologic Technology and Information Security Ministry of Education Xi'an China

ISBN: (纸本)9781665485081

Variational Autoencoder (VAE) is now popular in modeling and language generation tasks, which need to pay attention to the diversity of generation results. The existing models are insufficient in capturing the built-in relationships between topic representation and sequential words. At the same time, there is a massive contradiction between the commonly used simple Gaussian prior and the actual complex distribution of languages. To address the above problems, we introduce a hybrid Wasserstein Autoencoder (WAE) with Topic Embedded Representation (TER) for modeling. TER is obtained through an embedding-based topic model and can capture the dependencies and semantic similarities between topics and words. In this case, the learned latent variable has rich semantic knowledge with the help of TER and is easier to explain and control. Our experiments show that our method is competitive with other VAEs in modeling. © 2022 IEEE.

关键词： Semantics

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Attributes and Semantic Constrained GAN for Face Sketch-Photo Synthesis

Attributes and Semantic Constrained GAN for Face Sketch-Phot...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Jieying Zheng Haoxian Li Feng Liu Jiangsu Key Lab on Image Processing & Image Communications Nanjing University of Posts and Telecommunications Nanjing China School of Geographic and Biologic Information Nanjing University of Posts and Telecommunications Smart Health Big Data Analysis and Location Nanjing University of Posts and Telecommunications

ISBN: (纸本)9781665450867

Face sketch-photo synthesis aims at generating a facial photo conditioned on a given photo. It has attracted wide attention in computer vision and has been widely applied in law enforcement and entertainment. However, precisely synthesizing high-quality face photos is still challenging due to the missing color information in face sketches. To alleviate the problem, we propose an attribute and Semantic constrained Generative Adversarial Networks (ASGAN), which introduces face attribute and semantic constraints to improve the quality and accuracy of synthesized photos. Specifically, We first annotate the key attributes in the dataset and form a 14-dimensional attribute vector for each face. Then, the attribute features and semantic features obtained by face parsing are fused. Finally, the generator takes the fused constrain features and sketches as input and constructs two-stream encoders to synthesize high-quality photos. Extensive experimental results demonstrate that our method can significantly outperform state-of-the-art methods. It can synthe-size higher-quality face photos while maintaining the identity, attributes, and structure. Meanwhile, it can alleviate the problem of background and skin color synthesis errors.

关键词： Wireless communication Law enforcement Semantics Entertainment industry Color Signal processing Generative adversarial networks

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A New Composite Multimodality image Fusion Method Based on Shearlet Transform and Retina Inspired Model

A New Composite Multimodality Image Fusion Method Based on S...

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Iranian Machine Vision and image processing (MVIP)

作者： Mohammadmahdi Sayadi Hassan Ghassemian Reza Naimi Maryam Imani Image Processing and Information Analysis Lab. Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728168326

ISBN: (纸本)9781728168333

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