Dr. Jignesh S. Bhatt Profile

Dr. Jignesh S. Bhatt

Assistant Professor at Indian Institute of Information Technology Vadodara

SPIE Involvement:

Author

Area of Expertise:

Spectral unmixing of hyperspectral data , Registration of remotely sensed data , Gemoterical/Statistical data representation and modeling , Remote sensing data products , Machine/deep learning

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Jignesh S. Bhatt received the B.E. degree in Electronics and Communication from the North Gujarat University, Gujarat, India, in year 2000, the M.Tech. degree in Communication Systems from Sardar Vallabhbhai National Institute of Technology, Surat, India, in year 2008, and Ph.D. degree in Information and Communication Technology from Dhirubhai Ambani – Institute of Information and Communication Technology, Gandhinagar, India, in year 2015. His research interests include signal processing for remote sensing data, Multispectral and Hyperspectral satellite image analysis, estimation and optimization methods, Computer vision and Machine/deep learning.

He is currently serving as Assistant Professor at the Indian Institute of Information Technology Vadodara (IIITV) Gandhinagar. He has served for over fifteen years in different technical universities across India. He has long association working with Department of Space, ISRO, India, for various research projects related to satellite data analysis. He is a member of IEEE, a life member of the Indian Society for Technical Education and an associate member of the Institution of Engineers (India). He serves as a reviewer in many journals and transactions in his area of expertise.

Proceedings Article | 13 April 2018 Paper

Early detection of lung cancer from CT images: nodule segmentation and classification using deep learning

Manu Sharma, Jignesh Bhatt, Manjunath Joshi

Proceedings Volume 10696, 106960W (2018) https://doi.org/10.1117/12.2309530

KEYWORDS: Image segmentation, Computed tomography, Lung, Lung cancer, Image classification, Convolution, Convolutional neural networks, Databases, Cancer, Computer aided diagnosis and therapy

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Proceedings Article | 8 November 2012 Paper

A parametric statistical model over spectral space for the unmixing of imaging spectrometer data

Jignesh Bhatt, Manjunath Joshi, Mehul Raval

Proceedings Volume 8537, 85371J (2012) https://doi.org/10.1117/12.974645

KEYWORDS: Statistical analysis, Spectroscopy, Signal to noise ratio, Data modeling, Hyperspectral imaging, Mining, Hyperspectral simulation, Data acquisition, Imaging systems, Remote sensing

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The imaging spectrometer acquires hundreds of contiguous spectral measurements of an area. In many of the applications, the measured data is considered to be linear combinations of constituent materials signatures called endmembers. In practice the number of endmembers are usually much lesser than the total available spectral bands. One of the aims in the unmixing problem is to obtain underlying fractional abundances of every endmember at each location in the acquired scene. It should be noted that at every location, the nature of mixing among the endmembers is governed by the scene-content. It results in nonnegative abundance proportions for each endmember. Due to the presence of noise in the system the problem of unmixing becomes ill-posed. In this paper, we consider the variability in the mixing of endmembers over the acquired scene, for estimating the resultant abundances vectors given the data (measurements) and the endmembers. The relatively coarser instantaneous field of view (IFOV) covered by the hyperspectral imager are unmixed by using the spectral details available at each location in the scene. A Huber-Markov random field (HMRF) is considered across the contiguous spectral space, where a Huber function is defined to incorporate the abundances dependencies within the solution. As the Huber function imposes both the quadratic and the linear penalties, the HMRF preserves the smooth as well as sudden variations in the abundances. The problem is solved by using maximum a posteriori (MAP) estimation by incorporating the HMRF as the prior distribution on the abundances along with the likelihood function. The solution space is restricted to yield physically constrained abundances while optimizing. We conducted experiments on simulated data, constructed with regions having different classes of the mixtures of the endmembers signatures to evaluate the proposed approach. Then the execution is carried out by adding the noise in the data, and the results are compared with the state-of-art approach. Finally, the abundance maps are obtained for the well-known AVIRIS Cuprite mining site. The results validate the effectiveness of the proposed approach.

Proceedings Article | 8 November 2012 Paper

A regularization based method for spectral unmixing of imaging spectrometer data

Jignesh Bhatt, Manjunath Joshi, Mehul Raval

Proceedings Volume 8537, 85370J (2012) https://doi.org/10.1117/12.974649

KEYWORDS: Signal to noise ratio, Spectroscopy, Data modeling, Data acquisition, Remote sensing, Hyperspectral simulation, Image processing, Image analysis, Error analysis, Minerals

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