Introduction

This section provides an overview of key concepts and functionalities of the SiaPy library. You can find code snippets here.

To follow along with the examples, please download the example data from Zenodo and install the library by following the installation instructions.

📄 Data used in examples

The hyperspectral images were acquired using Hyspex push-broom cameras from Norsk Elektro Optikk (Oslo, Norway). Two spectral regions were covered:

• VNIR-1600: Visible to near-infrared (400–988 nm) with 160 bands and a bandwidth of 3.6 nm.
• SWIR-384: Short-wave infrared (950–2500 nm) with 288 bands and a bandwidth of 5.4 nm.

The acquired hyperspectral data, expressed as reflectance values, were already radiometrically calibrated to ensure accuracy and reliability for subsequent analysis.

Description of file names

File Name	Description
L1_L2_L3__test__ID_CAM.img	Image file
L1_L2_L3__test__ID_CAM.hdr	Header file corresponding to the image file

Component	Description
L	Labels of plants in the image
ID	Random ID assigned when the image is acquired
CAM	Camera type; corr for VNIR, corr2 for SWIR

Labels	Example
V-T-N	KK-K-04

Component	Description
V	Variety
	KK: KIS Krka
	KS: KIS Savinja
T	Treatment
	K: Control
	S: Drought
N	Index of a particular plant

🚀 Validation of setup

To verify that the data and the SiaPy installation meet the expected requirements, run the following code snippet. It should print a message indicating whether the loading was successful.

try:
    from pathlib import Path

    from siapy.entities import SpectralImageSet

    print("Libraries detected successfully.")
except ImportError as e:
    print(
        f"Error: {e}. Please ensure that the SiaPy library is installed and the environment is activated."
    )
    exit(1)

# Set the path to the directory containing the data
# !! ADJUST THIS PATH TO YOUR DATA DIRECTORY !!
data_dir = "./docs/examples/data"

# Find all header and image files in the data directory
header_paths = sorted(Path(data_dir).rglob("*.hdr"))
image_paths = sorted(Path(data_dir).rglob("*.img"))

# Create a SpectralImageSet from the found paths
image_set = SpectralImageSet.from_paths(
    header_paths=header_paths,
    image_paths=image_paths,
)

# Check if the data was loaded correctly
if len(image_set) > 0:
    print("Loading succeeded.")
else:
    print("Loading did not succeed.")

Warning

If the SiaPy library is not installed or the environment is not activated, an error will be thrown at the import statement. Additionally, if the data images are not present, the code will generate an empty image set, resulting in the script printing that the loading was not successful. Make sure to change data_dir to point to the directory where the images are stored.