Department of Computer Science and Information Systems

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Author: search.filters.author.Phartiyal, Gopal SinghHas files: No

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    Non iterative LDPC decoding by syndrome generation using artificial neural network
    (IEEE, 2016-07) Phartiyal, Gopal Singh
    Low density parity-check code (LDPC) is an error correcting code used in noisy communication channel (e.g. AWGN) to reduce the probability of error in information. By using LDPC codes, this probability can be made comparatively small, so that the data transmission rate can be as close to Shannon's limit. The decoding of Low Density Parity Check (LDPC) codes by iterative process of belief propagation gives challenges for designers looking for real time performance in communication systems. This thesis work proposes the use of Artificial Neural Networks (ANN) to replace belief propagation to approach closer to Shannon's limit more closer than other traditional decoding methods. This thesis is intended to design a new methodology to decode LDPC codes in Non-iterative manner with the help of ANN and Look Up Table (LUT). This work is at initial stage and will be extended for better performance.
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    A critical analysis of polarimetrie signatures on PALSAR 2 data for land cover classification
    (IEEE, 2016) Phartiyal, Gopal Singh
    In this paper, polarization signatures are extracted for utilization of the fully polarimetrie L-band ALOS-PALSAR 2 data. These signatures are extracted for different land cover classes (i.e., urban, water, short vegetation, tall vegetation and bare soil). Critical analysis is performed on the polarization signatures generated of different classes. Further, polarization signatures of different classes are compared with the help of normalized Euclidean distance (NED) and normalized signature correlation mapper (NSCM). Decision tree based algorithm is developed with the help of NSCM, NED and backscattered image for the classification of different land cover classes.
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    Optimal use of polarimetric signature on PALSAR-2 data for land cover classification
    (IEEE, 217) Phartiyal, Gopal Singh
    SAR data is playing key role in monitoring, the current status or change in, the land cover. For unsupervised SAR image classification, polarization signatures can play a significant role. Since it is difficult to obtain specific polarization signature of real land cover, it is customary to represent them with standard canonical structures polarization signatures. A critical analysis of the complex signatures of real targets is essential thereafter it is also a challenge to decide the thresholds or class boundary value on the correlation images. Therefore, in this paper an attempt has been made to critically analyze the polarimetric signature of complex targets and based on the correlation image analysis an OTSU multi-thresholding based approach is proposed to decide the individual class boundary values which will finally help in building a decision tree (DT) based classification technique. For this purpose L band fully polarimetric SAR data (PALSAR-2) has been used. DT class thresholds are computed using OTSU multi-thresholding method, scatter plot method, and a priori information. Obtained results reveal that complementary features like polarization signatures can help in identification as well as classification of land surface objects significantly by the proposed method.
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    Comparative study on deep neural network models for crop classification using time series polsar and optical data
    (ISPRS, 2018-11) Phartiyal, Gopal Singh
    Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a comparative analysis is made on the performances of various deep neural network (DNN) models for crop classification task using polarimetric synthetic aperture radar (PolSAR) and optical satellite data. For PolSAR data, Sentinel 1 dual pol SAR data is used. Sentinel 2 multispectral data is used as optical data. Five land cover classes including two crop classes of the season are taken. Time series data over the period of one crop cycle is used. Training and testing samples are measured and collected directly from the ground over the study region. Various convolutional neural network (CNN) and long short-term memory (LSTM) models are implemented, analysed, evaluated, and compared. Models are evaluated on the basis of classification accuracy and generalization performance.
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    A mixed spectral and spatial convolutional neural network for land cover classification using SAR and optical data
    (EGU 2018, 2018) Phartiyal, Gopal Singh
    Today, both SAR and optical data are available with good spatial and temporal resolutions. The two data modalities complement each other in many applications. There are numerous approaches to process the two data modalities, separately or combined. Domain or modality specific approaches such as polarimetric decomposition techniques or reflectance based techniques cannot work with the two datasets combined together. Data fusion approaches incur information loss during the process and are highly application specific. Machine learning (ML) approaches can operate on the combined dataset but have their own advantages and disadvantages. There is a need to explore new ML based approaches to achieve higher performance. Convolutional neural networks (CNNs) are young, trending, and promising ML tools in remote sensing applications. CNNs have the capability to learn complex features exclusively from data. Data from the two modalities can thus be brought together and processed with increased performance. In this paper an attempt is made to analyze CNN capabilities to perform land cover classification using multi-sensor data. SAR data used in this study is L band fully polarimetric PALSAR 2 data with 6 meter spatial resolution. Three basic polarimetric bands, namely, HH, HV, and VV, and four derived bands (polarization signatures) are used. Six multispectral Landsat 8 bands, pan sharpened and resampled at 6 meter spatial resolution, are used as optical data. All 13 features are stacked together and fed as input data to the proposed CNN. The areas selected for study are Haridwar and Roorkee regions of northern India. This study introduces a CNN where convolution is performed both spatially and spectrally. We show how this is an advantage over performing only spatial convolution. Five land cover classes namely, urban, bare soil, water, dense vegetation, and agriculture are considered. The CNN is trained on more than 1200 ground truth class data points measured directly on the terrain. The classification shows results with good generalization. Comparison with other classifiers such as SVMs shows that the proposed approach provides better classification results in terms of generalization, although the cross-validation accuracy is on the same order. The evaluation of the generalization of the classified image is done using ground truth knowledge on selected subset areas in the study area.
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    Improved utilization of polsar polarization signatures using convolutional-deep neural nets for land cover classification
    (IEEE, 2019-11) Phartiyal, Gopal Singh
    Normalized Euclidean distance (NED) and normalized signature correlation mapper (NSCM) are most popularly used pattern classifiers with polarization signatures (PSs) based polarimetric synthetic aperture radar (PolSAR) data applications. These methods are not able to fully exploit the PSs as they do not exploit the spatial context or pattern of PSs which is essential. Improved utilization of PSs is still required for PolSAR applications such as agriculture crop classification and monitoring. In this study, convolutional deep neural networks (C-DNNs) are introduced and utilized as pattern classifiers for PS classification. C-DNNs have the ability to consider and control the influence of local neighborhood pixels during classification. Therefore, in this study C-DNNs are utilized to extract and exploit subtle changes between PSs of land covers to improve classification performance. Comparison with NED and NSCM classifiers signify the contribution of C-DNNs by improved performance in PolSAR data classification.
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    An improved land cover classification using polarization signatures for PALSAR 2 data
    (Elsevier, 2020-06) Phartiyal, Gopal Singh
    Land cover classification in mixed land cover scenarios is challenging with PolSAR data. Polarimetric decomposition techniques are most popular methods for PolSAR data classification in recent times. These techniques focus on identification of dominant scattering phenomena and hence result in sub-optimal classification in mixed land cover scenarios. Alternatively, polarization signatures (PSs) are good illustrations of SAR target responses as they depict a detailed physical information from target backscatter. Researchers have successfully utilized SAR PSs for land cover (LC) classification. Some reports suggested utilizing correlation between observed PSs and standard target PSs as features for LC classification. This paper presents a study on improved utilization of PSs for optimal LC classification in mixed class scenarios. First, PS based SAR features are derived using fully polarimetric SAR data. The features represent a degree of similarity between observed and standard PSs. The derived features are termed as polarization signatures correlation features or PSCFs. The novel PSCFs are analyzed, evaluated and compared with decomposition based features for the purpose of LC classification. Classification performance indicators highlight potential of PSCFs for mixed LC classification problems. Therefore, further an adaptive and optimal LC class boundary estimation approach for LC classification is proposed and developed. Observed PSs and reference LC class PS statistics are used to build empirical models between classification performance indicators and LC class boundaries. The empirical models are optimized using the evolutionary genetic algorithm to maximize classification performance. A decision tree is constructed based on the optimal class boundaries to prepare LC classification. The proposed classification approach is compared with some recent popular classifiers and comparison suggests that the proposed approach provides satisfactory results for mixed LC classification scenarios.
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    Computational-vision based orthorectification and georefrencing for correct localization of railway track in UAV imagery
    (IEEE, 2021-02) Phartiyal, Gopal Singh
    In recent years, reliable rail track health monitoring and localization requires accurately orthorectified and georeferenced imagery. The vision-based approach is most suited for the geometrical correction of unmanned aerial vehicles (UAVs) high-resolution imagery in the given scenario. The single image acquired by UAV covers a significant area and contains only one reference point and many distorted pixels. This paper provides a novel computational vision-based approach for orthorectification and georeferencing of a single rail track aerial image among the set of given images without an exclusive reference map of that location and ground control points.
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    Permuted spectral and permuted spectral-spatial cnn models for polsar-multispectral data based land cover classification
    (Taylor & Francis, 2020-12) Phartiyal, Gopal Singh
    It is a challenge to develop methods which can process the polarimetric synthetic aperture radar (PolSAR) and multispectral (MS) data modalities together without losing information from either for remote sensing applications. This paper presents a study which attempts to introduce novel deep learning-based remote sensing data processing frameworks that utilize convolutional neural networks (CNNs) in both spatial and spectral domains to perform land cover (LC) classification with PolSAR-MS data. Also since earth observation remotely sensed data have usually larger spectral depth than normal camera image data, exploiting the spectral information in remote sensing (RS) data is crucial as well. In fact, convolutions in the sub-spectral space are intuitive and alternative to the process of feature selection. Recently, researchers have gained success in exploiting the spectral information of RS data, especially the hyperspectral data with CNNs. In this paper, exploitation of the spectral information in the PolSAR-MS data via a permuted localized spectral convolution along with localized spatial convolution is proposed. Further, the study in this paper also establishes the significance of performing permuted localized spectral convolutions over non-localized or localized spectral convolutions. Two models are proposed, namely a permuted local spectral convolutional network (Perm-LS-CNN) and a permuted local spectral-spatial convolutional network (Perm-LSS-CNN). These models are trained on ground truth class data points measured directly on the terrain. The evaluation of the generalization performance is done using ground truth knowledge on selected well-known regions in the study areas. Comparison with other popular machine learning classifiers shows that the Perm-LSS-CNN model provides better classification results in terms of both accuracy and generalization.
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    Introducing ISAP and MATSS: mental stress induced speech utterance procedure and obtained dataset
    (Elsevier, 2022-11) Phartiyal, Gopal Singh
    Mental stress persisting for long can cause severe health issues. There are various approaches available in the literature for investigating stress through speech utterances. The available procedure to obtain speech under stress dataset requires the speakers to undergo the actual stress situations in a real environment with limited control or inducing stress with a mental task in a lab environment. These approaches either suffer from ethical issues or unreliable labeling of the obtained speech samples. In this paper, we attempt to overcome these limitations with Induced mental Stress based speech production And labeling Procedure (ISAP), for obtaining speech utterances under mental stress along with labeling the samples simultaneously. The proposed ISAP can be incorporated by future studies as per their need to create a speech under stress dataset. We also present the obtained dataset, the baseline experiments, and classification results with various machine learning models. A total of 1260 speech utterances are obtained, with ISAP able to induce stress in 54.4% of the cases. The accuracy of the SVM classifier in recognizing three stress classes, namely, No Stress, Low Stress, and High Stress is found to be 57.1%.