Dr. Sanjeevi Shanmugam Profile

Dr. Sanjeevi Shanmugam

at Anna Univ Chennai

SPIE Involvement:

Author

SPIE Journal Paper | 5 December 2014

Super-resolution mapping of hyperspectral images for estimating the water-spread area of Peechi reservoir, southern India

Shanmuga Priyaa Sakthivel, Heltin Genitha C., Jeyakanthan Sivalingam, Sanjeevi Shanmugam

JARS, Vol. 8, Issue 01, 083510, (December 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083510

KEYWORDS: Super resolution, Neurons, Hyperspectral imaging, Image classification, Vegetation, Image analysis, Associative arrays, Error analysis, Image resolution, Neural networks

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Proceedings Article | 18 November 2014 Paper

Statistical and neural network analysis of hyperspectral radiometric data to characterise hematite of Singbhum iron ore belt, India

T. Magendran, Sanjeevi S.

Proceedings Volume 9263, 92630M (2014) https://doi.org/10.1117/12.2070388

KEYWORDS: Iron, Absorption, Statistical analysis, Hematite, Near infrared, Reflectivity, Radiometry, Neural networks, Mining, Chemical analysis

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The demand for iron ore has increased in the recent years, thereby necessitating the adoption of rapid and accurate approaches to iron ore exploration and its grade-assessment. It is in this context that hyperspectral radiometry is seen as a potential tool. This paper examines the potential of hyperspectral radiometry in the visible, NIR and SWIR regions of the EMR to assess the grades of hematite of the western Singhbhum iron ore belt of eastern India, in a rapid manner. Certain spectro-radiometric measurements and geochemical analysis were carried out and the results have been presented. From the spectral measurements, it is seen that the strength of reflectance and absorption at definite wavelength regions is controlled by the chemical composition of the iron ores. It is observed that the primary spectral characteristics of these hematite lie in the 650-750nm, 850 to 900nm and 2130-2230nm regions. The laboratory based hyperspectral signatures and multiple regression analysis of spectral parameters and geochemical parameters (Fe₂O₃% and Al₂O₃%) predicted the concentration of iron and alumina content in the hematite. A very strong correlation (R²=0.96) between the spectral parameters and Fe% in the hematite with a minimum error of 0.1%, maximum error of 7.4% and average error of 2.6% is observed. Similarly, a very strong correlation (R²=0.94) between the spectral parameters and Al₂O₃% in the iron ores with a minimum error of 0.04%, maximum error of 7.49% and average error of 2.5% is observed. This error is perhaps due to the presence of other components (SiO₂, TiO₂, P₂O etc.) in the samples which can alter the degree of reflectance and hence the spectral parameters. Neural network based multi-layer perception (MLP) analysis of various spectral parameters and geochemical parameters helped to understand the relative importance of the spectral parameters for predictive models. The strong correlations (Iron: R²=0.96; Alumina: R²=0.94) indicate that the laboratory hyperspectral signatures in the visible, NIR and SWIR regions can give a better estimate of the grades of hematite in a rapid manner.

Proceedings Article | 18 November 2014 Paper

Spectral matching in Hyperion images for improved characterization of Mangrove ecosystems in southern India

Padma S., Sanjeevi S.

Proceedings Volume 9263, 926317 (2014) https://doi.org/10.1117/12.2068938

KEYWORDS: Ecosystems, Algorithm development, Image classification, Distance measurement, Detection and tracking algorithms, Hyperspectral imaging, Remote sensing, Absorption, Stochastic processes

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Proceedings Article | 18 November 2014 Paper

Effectiveness of soft classification approaches as inputs for super resolution mapping of Hyperion image: a study on Peechi Reservoir, south India

Shanmuga Priyaa S., Sanjeevi S.

Proceedings Volume 9263, 926321 (2014) https://doi.org/10.1117/12.2068799

KEYWORDS: Super resolution, Neurons, Distance measurement, Vegetation, Neural networks, Image classification, Image resolution, Spatial resolution, Associative arrays, Error analysis

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Proceedings Article | 8 November 2014 Paper

Comparison of analogous terrestrial and Martian drainage systems: a remote sensing based study

Vaishali R., Sujita G., Sanjeevi S.

Proceedings Volume 9260, 926035 (2014) https://doi.org/10.1117/12.2068897

KEYWORDS: Mars, Geology, Remote sensing, Earth observing sensors, Image resolution, Mars Reconnaissance Orbiter, Mouth, Visualization, Landsat, Satellite imaging

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Proceedings Article | 21 October 2014 Paper

Hyperspectral band selection and classification of Hyperion image of Bhitarkanika mangrove ecosystem, eastern India

L. Ashokkumar, S. Shanmugam

Proceedings Volume 9239, 923914 (2014) https://doi.org/10.1117/12.2067483

KEYWORDS: Image fusion, Factor analysis, Wavelets, Image classification, Principal component analysis, Bismuth, Ecosystems, Hyperspectral imaging, Image quality, Image analysis

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Tropical mangrove forests along the coast evolve dynamically due to constant changes in the natural ecosystem and ecological cycle. Remote sensing has paved the way for periodic monitoring and conservation of such floristic resources, compared to labour intensive in-situ observations. With the laboratory quality image spectra obtained from hyperspectral image data, species level discrimination in habitats and ecosystems is attainable. One of the essential steps before classification of hyperspectral image data is band selection. It is important to eliminate the redundant bands to mitigate the problems of Hughes effect that are likely to affect further image analysis and classification accuracy. This paper presents a methodology for the selection of appropriate hyperspectral bands from the EO-1 Hyperion image for the identification and mapping of mangrove species and coastal landcover types in the Bhitarkanika coastal forest region, eastern India. Band selection procedure follows class based elimination procedure and the separability of the classes are tested in the band selection process. Individual bands are de-correlated and redundant bands are removed from the bandwise correlation matrix. The percent contribution of class variance in each band is analysed from the factors of PCA component ranking. Spectral bands are selected from the wavelength groups and statistically tested. Further, the band selection procedure is compared with similar techniques (Band Index and Mutual information) for validation. The number of bands in the Hyperion image was reduced from 196 to 88 by the Factor-based ranking approach. Classification was performed by Support Vector Machine approach. It is observed that the proposed Factor-based ranking approach performed well in discriminating the mangrove species and other landcover units compared to the other statistical approaches. The predominant mangrove species Heritiera fomes, Excoecaria agallocha and Cynometra ramiflora are spectral identified and the health status of these species are assessed by the selected band. Further, the performance of this band selection approaches are evaluated in multi-sensor image fusion for better mapping of mangrove ecosystems, wherein spatial resolution is enhanced while retaining the optimal number of hyperspectral bands.

Proceedings Article | 22 December 2006 Paper

Spectral unmixing of hyperspectral data to map bauxite deposits

Sanjeevi Shanmugam, P. Abhishekh

Proceedings Volume 6405, 64051N (2006) https://doi.org/10.1117/12.693862

KEYWORDS: Short wave infrared radiation, Minerals, Vegetation, Mining, Aluminum, Remote sensing, Reflectivity, Satellites, Absorption, Iron

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Showing 5 of 7 publications

Conference Committee Involvement (4)

Earth Observing Missions and Sensors: Development, Implementation, and Characterization IV

4 April 2016 | New Delhi, India

Earth Observing Missions and Sensors: Development, Implementation, and Characterization III

13 October 2014 | Beijing, China

Earth Observing Missions and Sensors: Development, Implementation, and Characterization II

30 October 2012 | Kyoto, Japan

Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications

13 November 2006 | Goa, India

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