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Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm
Research article (International Journal of Applied Earth Observation and Geoinformation, 2023) · cited 27× · AI/ML
Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm
Summary
Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm is a scholarly article[1].
Key Facts
Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm. Retrieved May 24, 2026, from https://4ort.xyz/entity/lithology-classification-in-semi-arid-area-combining-multi-source-remote-sensing-images-using-support-vector-machine-opt
MLA“Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/lithology-classification-in-semi-arid-area-combining-multi-source-remote-sensing-images-using-support-vector-machine-opt.
BibTeX@misc{4ortxyz_lithology-classification-in-semi-arid-area-combining-multi-source-remote-sensing-images-using-support-vector-machine-opt_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm}}, year = {2026}, url = {https://4ort.xyz/entity/lithology-classification-in-semi-arid-area-combining-multi-source-remote-sensing-images-using-support-vector-machine-opt}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm — https://4ort.xyz/entity/lithology-classification-in-semi-arid-area-combining-multi-source-remote-sensing-images-using-support-vector-machine-opt (retrieved 2026-05-24)
