Home ›
Entities
› academia
› Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm
Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm
Research article (Neural Computing and Applications, 2021) · cited 29× · AI/ML
Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm
Summary
Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm is a scholarly article[1].
Key Facts
Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm. Retrieved May 24, 2026, from https://4ort.xyz/entity/enhanced-classification-of-remotely-sensed-hyperspectral-images-through-efficient-band-selection-using-autoencoders-and-
MLA“Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/enhanced-classification-of-remotely-sensed-hyperspectral-images-through-efficient-band-selection-using-autoencoders-and-.
BibTeX@misc{4ortxyz_enhanced-classification-of-remotely-sensed-hyperspectral-images-through-efficient-band-selection-using-autoencoders-and-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm}}, year = {2026}, url = {https://4ort.xyz/entity/enhanced-classification-of-remotely-sensed-hyperspectral-images-through-efficient-band-selection-using-autoencoders-and-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm — https://4ort.xyz/entity/enhanced-classification-of-remotely-sensed-hyperspectral-images-through-efficient-band-selection-using-autoencoders-and- (retrieved 2026-05-24)
