Dr. Akhil Kallepalli Profile

Dr. Akhil Kallepalli

Postdoctoral Research Associate at Univ of Glasgow

SPIE Involvement:

Author

Area of Expertise:

Remote Sensing , Tissue Optics , Optical Physics , Biomedical Optics

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Company Website

I have a keen interest in a variety of topics revolving around beam shaping, image processing and light transport modelling. Specifically, in improving image acquisition, image reconstruction, light transport through media such as biological tissues and Monte Carlo methods from a simulation and theoretical perspective.

Proceedings Article | 28 June 2023 Open Access

Biophotonics for all: light transport through tissue

Graham Gibson, Akhil Kallepalli

Proceedings Volume 12723, 1272303 (2023) https://doi.org/10.1117/12.2664775

KEYWORDS: Light emitting diodes, Cameras, Biomedical optics, Tissues, Skin, Control systems, Near infrared, Electronic circuits, Visible radiation, Heatsinks

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Proceedings Article | 6 October 2021 Paper

Single-pixel imaging pattern sets and their implications on scene reconstruction

Akhil Kallepalli, John Innes, Graham Gibson, Miles Padgett

Proceedings Volume 11881, 118810X (2021) https://doi.org/10.1117/12.2601228

KEYWORDS: Spatial light modulators, Signal to noise ratio, Compressed sensing, Cameras, Sensors, Image restoration, Reconstruction algorithms, Modulators, Modulation, Image quality

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Proceedings Article | 6 October 2021 Paper

Open-source microscopic solution for classification of biological samples

Robert Archibald, Graham Gibson, Samuel Westlake, Akhil Kallepalli

Proceedings Volume 11879, 118790D (2021) https://doi.org/10.1117/12.2599435

KEYWORDS: Microscopes, Image classification, Data modeling, Machine learning, Microscopy, Medical research, Visualization, Mouth, Medicine, Machine vision

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Proceedings Article | 20 February 2020 Paper

Quantification and influence of skin chromophores for remote detection of anemic conditions

Akhil Kallepalli, David James

Proceedings Volume 11238, 112381B (2020) https://doi.org/10.1117/12.2545784

KEYWORDS: Blood, Skin, Tissues, Absorption, Scattering, Optical properties, Sensors, Monte Carlo methods, Chromophores, Photon transport

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SPIE Journal Paper | 14 January 2019

Implications of spectral and spatial features to improve the identification of specific classes

Akhil Kallepalli, Anil Kumar, Kourosh Khoshelham, David B. James, Mark A. Richardson

JARS, Vol. 13, Issue 01, 016504, (January 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.016504

KEYWORDS: Vegetation, Reflectivity, Databases, Near infrared, Image classification, Absorption, Feature extraction, Feature selection, Principal component analysis, Roads

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SPIE Journal Paper | 24 August 2017

Digital shoreline analysis system-based change detection along the highly eroding Krishna–Godavari delta front

Akhil Kallepalli, Nageswara Rao Kakani, David James, Mark Richardson

JARS, Vol. 11, Issue 03, 036018, (August 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.036018

KEYWORDS: Climate change, Geographic information systems, Earth observing sensors, Satellites, Satellite imaging, Statistical analysis, Coastal modeling, Temperature metrology, Projection systems, Landsat

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Proceedings Article | 18 October 2016 Presentation + Paper

Application of spectral and spatial indices for specific class identification in Airborne Prism EXperiment (APEX) imaging spectrometer data for improved land cover classification

Akhil Kallepalli, Anil Kumar, Kourosh Khoshelham, David James

Proceedings Volume 10005, 100050Z (2016) https://doi.org/10.1117/12.2241430

KEYWORDS: Prisms, Hyperspectral imaging, Principal component analysis, Image classification, Reflectivity, Vegetation, Databases, Near infrared, Absorption, Accuracy assessment

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Hyperspectral remote sensing's ability to capture spectral information of targets in very narrow bandwidths gives rise to many intrinsic applications. However, the major limiting disadvantage to its applicability is its dimensionality, known as the Hughes Phenomenon. Traditional classification and image processing approaches fail to process data along many contiguous bands due to inadequate training samples. Another challenge of successful classification is to deal with the real world scenario of mixed pixels i.e. presence of more than one class within a single pixel. An attempt has been made to deal with the problems of dimensionality and mixed pixels, with an objective to improve the accuracy of class identification. In this paper, we discuss the application of indices to cope with the disadvantage of the dimensionality of the Airborne Prism EXperiment (APEX) hyperspectral Open Science Dataset (OSD) and to improve the classification accuracy using the Possibilistic c–Means (PCM) algorithm. This was used for the formulation of spectral and spatial indices to describe the information in the dataset in a lesser dimensionality. This reduced dimensionality is used for classification, attempting to improve the accuracy of determination of specific classes. Spectral indices are compiled from the spectral signatures of the target and spatial indices have been defined using texture analysis over defined neighbourhoods. The classification of 20 classes of varying spatial distributions was considered in order to evaluate the applicability of spectral and spatial indices in the extraction of specific class information. The classification of the dataset was performed in two stages; spectral and a combination of spectral and spatial indices individually as input for the PCM classifier. In addition to the reduction of entropy, while considering a spectral-spatial indices approach, an overall classification accuracy of 80.50% was achieved, against 65% (spectral indices only) and 59.50% (optimally determined principal components).

Proceedings Article | 18 October 2016 Presentation + Paper

Remote sensing and GIS analysis for demarcation of coastal hazard line along the highly eroding Krishna-Godavari delta front

Akhil Kallepalli, Nageswara Rao Kakani, David James

Proceedings Volume 10005, 1000507 (2016) https://doi.org/10.1117/12.2239485

KEYWORDS: Landsat, Geographic information systems, Remote sensing, Coastal modeling, Statistical analysis, Hazard analysis, Climate change, Earth observing sensors, Satellites, Satellite imaging

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