---
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}
}
```