Book ChapterDOI
Revisiting hyperspectral remote sensing: origin, processing, applications and way forward
Prashant K. Srivastava,Ramandeep Kaur M. Malhi,Prem Chandra Pandey,Akash Anand,Prachi Singh,Manish Kumar Pandey,Ayushi Gupta +6 more
- pp 3-21
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TLDR
This chapter discusses the origin of hyperspectral remote sensing, its importance, preprocessing, inversion models suitable for hyperspectrals, as well as several possible applications, including but not limited to, vegetation analysis, agriculture, urban, water quality, and mineral identification.Abstract:
After several years of research and development in hyperspectral imaging systems that enriched our knowledge and enhanced our capacity to explore the Earth, these systems have been widely accepted by the remote sensing community. They have evolved as major techniques and have now entered the mainstream of the earth observation data users. This chapter discusses the origin of hyperspectral remote sensing, its importance, preprocessing, inversion models suitable for hyperspectral datasets, as well as several possible applications, including but not limited to, vegetation analysis, agriculture, urban, water quality, and mineral identification. The chapter concludes by looking at the way forward for hyperspectral remote sensing.read more
Citations
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Journal ArticleDOI
Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India
Ramandeep Kaur M. Malhi,Sultanova Umida Rustamovna,Akash Anand,Prashant K. Srivastava,Sumit Kumar Chaudhary,Manish Kumar Pandey,Mukund Dev Behera,Amit Kumar,Prachi Singh,G. Sandhya Kiran +9 more
TL;DR: In this paper, two nonparametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions were employed for the prediction of above ground biomass using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA).
Journal ArticleDOI
An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas
Ramandeep Kaur M. Malhi,Akash Anand,Prashant Srivastava,G. Sandhya Kiran,George P. Petropoulos,Christos Chalkias +5 more
TL;DR: This study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.
Journal ArticleDOI
Enhanced classification of hyperspectral images using improvised oversampling and undersampling techniques
TL;DR: The current work explored the solution to handle class imbalance by resampling the datasets before the application of classification algorithms by proposing a new computationally efficient class wise resampled technique which is based on SMOTE and centroid-based clustering.
Journal ArticleDOI
Band selection algorithms for foliar trait retrieval using AVIRIS-NG: a comparison of feature based attribute evaluators
Ramandeep Kaur M. Malhi,Manish Kumar Pandey,Akash Anand,Prashant K. Srivastava,George P. Petropoulos,Prachi Singh,G. Sandhya Kiran,B. K. Bhattarcharya +7 more
TL;DR: Interband information overlapping enhances redundancy in hyperspectral data, which makes identification of application-specific optimal bands essential for obtaining accurate information about folia...
Journal ArticleDOI
Optimal band characterization in reformation of hyperspectral indices for species diversity estimation
Akash Anand,Sultanova Umida Rustamovna,Ramandeep Kaur M. Malhi,Prashant K. Srivastava,Prachi Singh,Ashwini N. Mudaliar,George P. Petropoulos,G. Sandhya Kiran +7 more
TL;DR: In this article, the authors provided modified hyperspectral indices through detection of optimum bands for estimating species diversity within Shoolpaneshwar Wildlife Sanctuary (SWS) in India.
References
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Journal ArticleDOI
Hyperspectral remote sensing of plant pigments.
TL;DR: The prospects for extending research to the wider range of pigments in addition to chlorophyll are examined, testing emerging methods of hyperspectral analysis and exploring the fusion of hypersportral and LIDAR remote sensing.
Software for the derivation of scaled surface reflectances from AVIRIS data
TL;DR: An operational software program for deriving "scaled surface reflectances" from spectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is presented in this article.
Journal ArticleDOI
Hyperspectral remote sensing of foliar nitrogen content.
Yuri Knyazikhin,Mitchell A. Schull,Pauline Stenberg,Matti Mõttus,Miina Rautiainen,Yan Yang,Alexander Marshak,Pedro Latorre Carmona,Robert K. Kaufmann,Philip Lewis,Mathias Disney,Vern C. Vanderbilt,Anthony B. Davis,Frédéric Baret,Stéphane Jacquemoud,Alexei Lyapustin,Ranga B. Myneni +16 more
TL;DR: It is found that to infer leaf biochemical constituents, e.g., N content, from remotely sensed data, BRF spectra in the interval 710–790 nm provide critical information for correction of structural influences, and suggests that surface characteristics of leaves impact remote sensing of its internal constituents.
Journal ArticleDOI
Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean
TL;DR: A review of hyperspectral atmospheric correction techniques is presented in this paper, where issues related to spectral smoothing are discussed and improvements to the present atmospheric correction algorithms, mainly the addition of a module for modeling atmospheric nitrogen dioxide absorption effects in the visible, are given.
Journal ArticleDOI
Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features
TL;DR: In this paper, the authors investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species.
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