Publication2021
Spectral & spatial feature detection on Hyperspectral images using deep neural networks
The record
- Type
- Publication
- Date
- 5 August 2021
- Level
- National
- Journal
- 2nd International Conference on Range Technology Icort 2021
- Published
- 5 August 2021
- DOI
- 10.1109/icort52730.2021.9581685
- Scopus ID
- 85119010979
- Authors
- 3
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
15 Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
Hyperspectral CNN-LSTM for remote sensing image classification enables land-use land-cover assessment for ecosystem monitoring.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Deep learning research on satellite imagery classification advances remote sensing technology.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
AI-based satellite data analysis supports environmental monitoring and terrestrial ecosystem assessment.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Spectral & spatial feature detection on Hyperspectral images using deep neural networks”. Kortex.