Roozbeh Rajabi

University of Genoa

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Hassan Ghassemian

Tarbiat Modares University

Hadi Zayyani

Qom University of Technology

Abouzar Estebsari

London South Bank University

Sara Khoshsokhan

Qom University of Technology

Mohammad Irani Azad

Shahid Beheshti University

Mahdi Khodadadzadeh

University of Twente

Elahe Khoshbakhti Vaygan

Qom University of Technology

Amin Zehtabian

Freie Universität Berlin

Mehraban Shahi

Hormozgan University of Medical Sciences

Seyed Hossein Mosavi Azarang

Qom University of Technology

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Qom University of Technology

University of Genoa

CIMA Research Foundation

Tarbiat Modares University

Polytechnic University of Turin

Iran University of Science and Technology

Shahid Beheshti University

London South Bank University

Shahed University

University of Chinese Academy of Sciences

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Editorial: Hyperspectral imaging in environmental monitoring and analysis

Frontiers in Environmental Science (2024)

Roozbeh Rajabi Amin Zehtabian Keshav D. Singh Alireza Tabatabaeenejad Pedram Ghamisi

EDITORIAL article Front. Environ. Sci., 19 January 2024Sec. Environmental Informatics and Remote Sensing Volume 11 - 2023 | https://doi.org/10.3389/fenvs.2023.1353447

Environmental Monitoring

10.3389/fenvs.2023.1353447

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Citations (10)

Fusion of Hyperspectral and Panchromatic Images using Spectral Uumixing Results

arXiv (Cornell University) (2013)

Roozbeh Rajabi Hassan Ghassemian

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spectral unmixing of mixed pixels in hyperspectral images results in spectral signature and abundance fractions of endmembers but gives no information about their location in a mixed pixel. In this paper we have used spectral unmixing results of hyperspectral images and segmentation results of panchromatic image for data fusion. The proposed method has been applied on simulated data using AVRIS Indian Pines datasets. Results show that this method can effectively combine information in hyperspectral and panchromatic images.

Panchromatic film

Full spectral imaging

Spectral signature

Spectral resolution

Source

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Citations (1)

Hyperspectral data unmixing using GNMF method and sparseness constraint

Roozbeh Rajabi Hassan Ghassemian

Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem. In this paper we have used graph regularized (GNMF) method with sparseness constraint to unmix hyperspectral data. This method applied on simulated data using AVIRIS Indian Pines dataset and USGS library and results are quantified based on AAD and SAD measures. Results in comparison with other methods show that the proposed method can unmix data more effectively.

Endmember

Non-negative Matrix Factorization

Abundance estimation

Data set

10.1109/igarss.2013.6723058

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Citations (20)

Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data

Journal of the Indian Society of Remote Sensing (2014)

Roozbeh Rajabi Hassan Ghassemian

Endmember

Non-negative Matrix Factorization

Cuprite

10.1007/s12524-014-0408-2

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Citations (21)

Sequence-to-Sequence Model with Transformer-based Attention Mechanism and Temporal Pooling for Non-Intrusive Load Monitoring

2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) (2023)

Mohammad Irani Azad Roozbeh Rajabi Abouzar Estebsari

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency.

Pooling

Sequence (biology)

10.1109/eeeic/icpseurope57605.2023.10194819

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Citations (1)

Unmixing of Hyperspectral Data Using Robust Statistics-based NMF

arXiv (Cornell University) (2012)

Roozbeh Rajabi Hassan Ghassemian

Mixed pixels are presented in hyperspectral images due to low spatial resolution of hyperspectral sensors. Spectral unmixing decomposes mixed pixels spectra into endmembers spectra and abundance fractions. In this paper using of robust statistics-based nonnegative matrix factorization (RNMF) for spectral unmixing of hyperspectral data is investigated. RNMF uses a robust cost function and iterative updating procedure, so is not sensitive to outliers. This method has been applied to simulated data using USGS spectral library, AVIRIS and ROSIS datasets. Unmixing results are compared to traditional NMF method based on SAD and AAD measures. Results demonstrate that this method can be used efficiently for hyperspectral unmixing purposes.

Non-negative Matrix Factorization

Endmember

Abundance estimation

10.48550/arxiv.1212.0888

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Citations (0)

Multilayer structured NMF for spectral unmixing of hyperspectral images

Roozbeh Rajabi Hassan Ghassemian

One of the challenges in hyperspectral data analysis is the presence of mixed pixels. Mixed pixels are the result of low spatial resolution of hyperspectral sensors. Spectral unmixing methods decompose a mixed pixel into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fraction values, NMF based methods are well suited to this problem. In this paper multilayer NMF has been used to improve the results of NMF methods for spectral unmixing of hyperspectral data under the linear mixing framework. Sparseness constraint on both spectral signatures and abundance fractions matrices are used in this paper. Evaluation of the proposed algorithm is done using synthetic and real datasets in terms of spectral angle and abundance angle distances. Results show that the proposed algorithm outperforms other previously proposed methods.

Non-negative Matrix Factorization

Endmember

Abundance estimation

Data set

Spectral signature

Full spectral imaging

Spectral resolution

10.1109/whispers.2014.8077521

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Citations (5)

Hyperspectral Unmixing Based on Clustered Multitask Networks

arXiv (Cornell University) (2018)

Sara Khoshsokhan Roozbeh Rajabi Hadi Zayyani

Hyperspectral remote sensing is a prominent research topic in data processing. Most of the spectral unmixing algorithms are developed by adopting the linear mixing models. Nonnegative matrix factorization (NMF) and its developments are used widely for estimation of signatures and fractional abundances in the SU problem. Sparsity constraints was added to NMF, and was regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing. In the proposed algorithm, a network including clusters is employed. Each pixel in the hyperspectral images considered as a node in this network. The proposed algorithm is optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods.

Non-negative Matrix Factorization

Spectral signature

10.48550/arxiv.1812.10788

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Citations (0)

Electricity Price Forecasting Model based on Gated Recurrent Units

2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) (2022)

Nafise Rezaei Roozbeh Rajabi Abouzar Estebsari

The participation of consumers and producers in demand response programs has increased in smart grids, which reduces investment and operation costs of power systems. Also, with the advent of renewable energy sources, the electricity market is becoming more complex and unpredictable. To effectively implement demand response programs, forecasting the future price of electricity is very crucial for producers in the electricity market. Electricity prices are very volatile and change under the influence of various factors such as temperature, wind speed, rainfall, intensity of commercial and daily activities, etc. Therefore, considering the influencing factors as dependent variables can increase the accuracy of the forecast. In this paper, a model for electricity price forecasting is presented based on Gated Recurrent Units. The electrical load consumption is considered as an input variable in this model. Noise in electricity price seriously reduces the efficiency and effectiveness of analysis. Therefore, an adaptive noise reducer is integrated into the model for noise reduction. The SAEs are then used to extract features from the de-noised electricity price. Finally, the de-noised features are fed into the GRU to train predictor. Results on real dataset shows that the proposed methodology can perform effectively in prediction of electricity price.

Electricity Price Forecasting

Investment

Demand Response

Electricity retailing

Variable renewable energy

10.1109/eeeic/icpseurope54979.2022.9854746

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Citations (7)

Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Electronics (2024)

Mohammad Irani Azad Roozbeh Rajabi Abouzar Estebsari

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurately discern energy consumption patterns of individual household appliances. The proposed method entails a sequence of layers, including encoders, transformers, attention, temporal pooling, and residual connections, offering a comprehensive solution for NILM while effectively capturing appliance-specific energy usage in a household. The proposed model was evaluated using UK-DALE, REDD, and REFIT datasets in both seen and unseen cases. It shows that the proposed model in this paper performs better than other methods stated in other papers in terms of F1-score and total error of the results (in terms of SAE). This model achieved an F1-score equal to 92.96 as well as a total SAE equal to −0.036, which shows its effectiveness in accurately diagnosing and estimating the energy consumption of individual home appliances. The findings of this research show that the proposed model can be a tool for energy management in residential and commercial buildings.

Pooling

10.3390/electronics13020407

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Citations (2)