Hyperspectral Remote Sensing

Section 1 Introduction to Hyperspectral Remote Sensing and Principles of Theory and Data Processing<br>1. Revisiting hyperspectral remote sensing: origin, processing, applications and way forward<br>2. Spectral smile correction for airborne imaging spectrometers<br>3. Anomaly detection in hyperspectral remote sensing images<br>4. Atmospheric parameter retrieval and correction using hyperspectral data<br>5. Hyperspectral image classifications and feature selection<br>Section 2 Hyperspectral Remote Sensing Application in Vegetation<br>6. Identification of functionally distinct plants using linear spectral mixture analysis<br>7. Estimation of chengal trees relative abundance using coarse spatial resolution hyperspectral systems<br>8. Hyperspectral remote sensing in precision agriculture: present status, challenges, and future trends<br>9. Discriminating tropical grasses grown under different nitrogen fertilizer regimes in KwaZulu-Natal, South Africa<br>Section 3 Hyperspectral Remote Sensing Application in Water, Snow, Urban Research<br>10. Effect of contamination and adjacency factors on snow using spectroradiometer and hyperspectral images<br>11. Remote sensing of inland water quality: a hyperspectral perspective<br>12. Efficacy of hyperspectral data for monitoring and assessment of wetland ecosystem<br>Section 4 Hyperspectral Remote Sensing Application in Soil and Mineral Exploration<br>13. Spectroradiometry as a tool for monitoring soil contamination by heavy metals in a floodplain site<br>14. Hyperspectral remote sensing applications in soil: a review<br>15. Mineral exploration using hyperspectral data<br>16. Metrological hyperspectral image analysis through spectral differences<br>Section 5 Hyperspectral Remote Sensing: Multi-sensor, Fusion and Indices applications for Pollution<br>Detection and Other Applications<br>17. Improving the detection of cocoa bean fermentation-related changes using image fusion<br>18. Noninvasive detection of plant parasitic nematodes using hyperspectral and other remote sensing systems<br>19. Evaluating the performance of vegetation indices for detecting oil pollution effects on vegetation using hyperspectral (Hyperion EO-1) and multispectral (Sentinel-2A) data in the Niger Delta<br>20. Hyperspectral vegetation indices to detect hydrocarbon pollution<br>Section 6 Hyperspectral Remote Sensing: Challenges, Future Pathway for Research & Emerging Applications<br>21. Future perspectives and challenges in hyperspectral remote sensing