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---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': dense forest
'1': grassland
'2': harbor
'3': high buildings
'4': low buildings
'5': overpass
'6': railway
'7': residential area
'8': roads
'9': sparse forest
'10': storage tanks
splits:
- name: train
num_bytes: 969136595.28
num_examples: 1232
download_size: 916398984
dataset_size: 969136595.28
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "RS_C11"
## Dataset Description
- **Paper** [Feature significance-based multibag-of-visual-words model for remote sensing image scene classification](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-3/035004/Feature-significance-based-multibag-of-visual-words-model-for-remote/10.1117/1.JRS.10.035004.pdf)
### Licensing Information
Free usage without license.
## Citation Information
[Feature significance-based multibag-of-visual-words model for remote sensing image scene classification](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-3/035004/Feature-significance-based-multibag-of-visual-words-model-for-remote/10.1117/1.JRS.10.035004.pdf)
```
@article{zhao2016feature,
title = {Feature significance-based multibag-of-visual-words model for remote sensing image scene classification},
author = {Zhao, Lijun and Tang, Ping and Huo, Lianzhi},
year = 2016,
journal = {Journal of Applied Remote Sensing},
publisher = {Society of Photo-Optical Instrumentation Engineers},
volume = 10,
number = 3,
pages = {035004--035004}
}
``` |