# Review of deep learning methods for remote sensing satellite images classification: experimental survey and comparative analysis

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/review-of-deep-learning-methods-for-remote-sensing-satellite-images/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Adekanmi Adeyinka Adegun,Serestina Viriri,Jules‐Raymond Tapamo |
| Citations | 205 |
| DOI | 10.1186/s40537-023-00772-x |
| Fields | Computer Science,Earth and Planetary Sciences,Engineering |
| Open Access | true |
| OA Status | gold |
| OA URL | https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-023-00772-x |
| OpenAlex ID | https://openalex.org/W4379229760 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Adekanmi Adeyinka Adegun](https://scholariq.org/researchers/adekanmi-adeyinka-adegun/)
- [Serestina Viriri](https://scholariq.org/researchers/serestina-viriri/)

## Paper primary topic

- [Remote-Sensing Image Classification](https://scholariq.org/topics/remote-sensing-image-classification/)

## Paper topics

- [Remote-Sensing Image Classification](https://scholariq.org/topics/remote-sensing-image-classification/)
- [Remote Sensing and Land Use](https://scholariq.org/topics/remote-sensing-and-land-use/)
- [Geochemistry and Geologic Mapping](https://scholariq.org/topics/geochemistry-and-geologic-mapping/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.