Publication2024
Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification
- Industry, Innovation and Infrastructure
- Innovation, Science and Technology
The record
- Type
- Publication
- Date
- 1 January 2024
- Level
- National
- Journal
- International Journal of Computing and Digital Systems
- Volume
- 16
- Issue
- 1
- Pages
- 719-738
- Published
- 1 January 2024
- DOI
- 10.12785/ijcds/160153
- Scopus ID
- 85200802940
- ISSN
- 2210142X
- Authors
- 3
- Open access
- Yes
- Citations
- 6
- International co-authorship
- No
- Co-author countries
- IN
Classification
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UN Sustainable Development Goals
9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Proposes deep feature segmentation model using hybrid convolution networks for hyperspectral image classification achieving 99.95% accuracy
- 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
Viksit Bharat @2047
vb-3 Innovation, Science and Technology
Derived through the seeded SDG-to-Viksit-Bharat crosswalk from the primary SDG 9 label. Proposes deep feature segmentation model using hybrid convolution networks for hyperspectral image classification achieving 99.95% accuracy
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
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- 1
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Citing this record
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KIIT Deemed to be University. “Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification”. Kortex.