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
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
Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data
Prashant K. Srivastava,Manika Gupta,Ujjwal Singh,Rajendra Prasad,Prem Chandra Pandey,Akhilesh Singh Raghubanshi,George P. Petropoulos +6 more
TL;DR: In this paper, the authors evaluated the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices.
Journal ArticleDOI
Estimate leaf chlorophyll of rice using reflectance indices and partial least squares
Journal ArticleDOI
Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data
Laura Tusa,Mahdi Khodadadzadeh,Cecilia Contreras,Kasra Rafiezadeh Shahi,Margret C. Fuchs,Richard Gloaguen,Jens Gutzmer +6 more
TL;DR: The upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning) and upscale the quantitative SEM-MLA mineralogical data to drill-core scale.
Journal ArticleDOI
Synergetic use of in situ and hyperspectral data for mapping species diversity and above ground biomass in Shoolpaneshwar Wildlife Sanctuary, Gujarat
Ramandeep Kaur M. Malhi,Akash Anand,Ashwini N. Mudaliar,Prem Chandra Pandey,Prashant K. Srivastava,G. Sandhya Kiran +5 more
TL;DR: In this paper, the authors used quadrat sampling in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, which was used to compute Shannon-Weiner Diversity Index (H′), above ground biomass (AGB) was calculated measuring the Height and Diameter at Breast Height (DBH) of different trees in the sampling plots.
Journal ArticleDOI
Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands
TL;DR: The spectral variability within the 420–480 nm and 760–900 nm ranges, the first derivative value at the sensitive bands, and the normalized difference at narrow spectral ranges correlated well with plant species richness.
Related Papers (5)
An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines
Alpana Shukla,Rajsi Kot +1 more