Crate oxigdal_sensors 

OxiGDAL Sensors - Remote Sensing and Satellite Sensor Data Processing

This crate provides comprehensive support for remote sensing and satellite sensor data processing, including sensor definitions, radiometric corrections, spectral indices, and image classification.

Features

Sensor Support

Comprehensive sensor definitions for major satellite platforms:

• Landsat: 5 TM, 7 ETM+, 8/9 OLI/TIRS
• Sentinel: 2 MSI (optical), 1 SAR
• MODIS: Terra/Aqua (36 bands)
• ASTER: VNIR, SWIR, and TIR subsystems

use oxigdal_sensors::sensors::landsat;

let sensor = landsat::landsat8_oli_tirs();
assert_eq!(sensor.bands.len(), 11);

let red_band = sensor.get_band_by_common_name("Red");
assert!(red_band.is_some());

Radiometric Calibration

Convert Digital Numbers (DN) to physical units:

use oxigdal_sensors::radiometry::calibration::{RadiometricCalibration, earth_sun_distance};
use scirs2_core::ndarray::array;

let cal = RadiometricCalibration::new(0.00002, 0.0)
    .with_solar_irradiance(1554.0);

let dn = array![[1000.0, 2000.0], [3000.0, 4000.0]];
let radiance = cal.dn_to_radiance(&dn.view());

let doy = earth_sun_distance(180);
assert!(doy.is_ok());

Atmospheric Correction

Multiple atmospheric correction methods:

use oxigdal_sensors::radiometry::atmospheric::{DarkObjectSubtraction, AtmosphericCorrection};
use scirs2_core::ndarray::array;

let dos = DarkObjectSubtraction::default_params();
let toa = array![[0.05, 0.10], [0.15, 0.20]];

let corrected = dos.correct(&toa.view());
assert!(corrected.is_ok());

Spectral Indices (20+)

Comprehensive collection of spectral indices:

Vegetation Indices

• NDVI, EVI, EVI2, SAVI, MSAVI, OSAVI
• GNDVI, GRVI, CI (Chlorophyll Index)
• NDWI, NDMI (water content)

use oxigdal_sensors::indices::vegetation::{ndvi, evi, savi};
use scirs2_core::ndarray::array;

let nir = array![[0.5, 0.6], [0.7, 0.8]];
let red = array![[0.1, 0.1], [0.1, 0.1]];

let ndvi_result = ndvi(&nir.view(), &red.view());
assert!(ndvi_result.is_ok());

let blue = array![[0.05, 0.05], [0.05, 0.05]];
let evi_result = evi(&nir.view(), &red.view(), &blue.view());
assert!(evi_result.is_ok());

Burn Indices

• nbr, d_nbr, nbr2

use oxigdal_sensors::indices::burn::{nbr, d_nbr};
use scirs2_core::ndarray::array;

let nir = array![[0.5, 0.6]];
let swir2 = array![[0.2, 0.2]];

let nbr_result = nbr(&nir.view(), &swir2.view());
assert!(nbr_result.is_ok());

Urban Indices

• NDBI, UI, IBI

Water Indices

• MNDWI, AWEI, WRI

Pan-Sharpening

Multiple pan-sharpening algorithms:

• Brovey Transform
• IHS (Intensity-Hue-Saturation)
• PCA (Principal Component Analysis)

Image Classification

• Unsupervised: K-Means, ISODATA
• Supervised: Maximum Likelihood

COOLJAPAN Policy Compliance

This crate adheres to COOLJAPAN ecosystem policies:

• Pure Rust: 100% Pure Rust implementation, no C/Fortran dependencies
• No unwrap(): All error cases are properly handled with Result<T, E>
• SciRS2 Integration: Uses scirs2-core for scientific computing
• Comprehensive Error Handling: Custom error types for all failure modes
• File Size Policy: All source files are kept under 2000 lines
• Latest Crates: Uses latest versions from crates.io
• Workspace Policy: Follows workspace dependency management

Performance

All algorithms are optimized for production use with:

• Efficient memory usage
• Vectorized operations using ndarray
• Optional parallel processing (enable parallel feature)
• Numerical stability checks

Safety

This crate is designed with safety in mind:

• No unsafe code (except what’s in dependencies)
• Comprehensive input validation
• Proper handling of edge cases
• Clear error messages for debugging

Re-exports

pub use error::Result;

pub use error::SensorError;

Modules

classification

Image classification algorithms

error

Error types for OxiGDAL Sensors

indices

Spectral indices for remote sensing

pan_sharpening

Pan-sharpening algorithms

radiometry

Radiometric corrections

sensors

Sensor definitions and characteristics

Constants

NAME

Crate name

VERSION

Crate version