mlresearch.datasets.RemoteSensingDatasets

class mlresearch.datasets.RemoteSensingDatasets(names: str = 'all', data_home: str = None, download_if_missing: bool = True)

Class to download, transform and save remote sensing datasets.

download()

Download the datasets and append undersampled versions of them.

fetch_botswana()

Download and transform the Botswana Data Set. Label “0” means the pixel is not labelled. It is therefore dropped.

http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Botswana

fetch_indian_pines()

Download and transform the Indian Pines Data Set. Label “0” means the pixel is not labelled. It is therefore dropped.

http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Indian_Pines

fetch_kennedy_space_center()

Download and transform the Kennedy Space Center Data Set. Label “0” means the pixel is not labelled. It is therefore dropped.

http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Kennedy_Space_Center_.28KSC.29

fetch_pavia_centre()

Download and transform the Pavia Centre Data Set. Label “0” means the pixel is not labelled. It is therefore dropped.

http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_Centre_scene

fetch_pavia_university()

Download and transform the Pavia University Data Set. Label “0” means the pixel is not labelled. It is therefore dropped.

http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_University_scene

fetch_salinas()

Download and transform the Salinas Data Set. Label “0” means the pixel is not labelled. It is therefore dropped.

http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Salinas_scene

fetch_salinas_a()

Download and transform the Salinas-A Data Set. Label “0” means the pixel is not labelled. It is therefore dropped.

http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Salinas-A_scene

imbalance_datasets(imbalance_ratio: float, random_state: int = None)

Appends imbalanced versions of datasets with predefined imbalance ratios to self.content_.

\[IR = \frac{|C_{maj}|}{|C_{min}|}\]

Parameters:

imbalance_ratiofloat

Final Imbalance Ratio expected in the datasets.

random_stateint, RandomState instance, default=None

Control the randomization of the algorithm.

• If int, random_state is the seed used by the random number generator;

• If RandomState instance, random_state is the random number generator;

• If None, the random number generator is the RandomState instance used by np.random.

Returns:

selfDatasets

items()

keys()

save(path, db_name)

Save datasets.

summarize_datasets()

Create a summary of the downloaded datasets.

Returns:

datasets_summarypd.DataFrame

Dataframe with summary statistics of all datasets.

values()