NDIToolbox: A Complete Guide to Multispectral Image ProcessingMultispectral image processing is essential in remote sensing, environmental monitoring, agriculture, and many scientific fields. NDIToolbox is an open-source collection of tools built on top of NumPy and SciPy that simplifies working with multispectral (and hyperspectral) imagery by providing functions for reading, preprocessing, visualizing, and analyzing multi-band images. This guide walks through NDIToolbox’s core features, typical workflows, example use cases, and tips for getting the most from the library.
What is NDIToolbox?
NDIToolbox is a Python-based toolkit designed to make processing of multispectral and hyperspectral imagery more accessible. It leverages the scientific Python stack—NumPy, SciPy, matplotlib, and rasterio/gdal where needed—to provide utilities for common remote sensing tasks: reading diverse image formats, performing radiometric corrections, applying atmospheric compensation, computing spectral indices, classification, and visualization.
NDIToolbox aims to balance simplicity and flexibility: it gives high-level convenience functions for routine tasks and lower-level primitives for building custom pipelines.
Key features
- Reading and writing common raster formats (GeoTIFF, ENVI, NetCDF) with associated metadata support.
- Band indexing and band math utilities for rapid spectral computations.
- Radiometric normalization, reflectance conversion, and simple atmospheric corrections.
- Spatial preprocessing: resampling, reprojecting, mosaicking, and subset extraction.
- Noise reduction and filtering (e.g., median, Gaussian, Savitzky–Golay for spectral smoothing).
- Vegetation and land-surface indices (NDVI, EVI, SAVI, NDWI, etc.) implemented as easy functions.
- Unsupervised and supervised classification helpers (k-means, Gaussian mixture models, Random Forest interface).
- Dimensionality reduction (PCA, MNF) and endmember extraction approaches.
- Visualization helpers for RGB/False-color composites, spectral profiles, and interactive plotting.
- Batch processing and pipeline structuring utilities.
Installation
NDIToolbox is installable via pip or conda. Typical installation:
pip install nditoolbox Or with conda-forge:
conda install -c conda-forge nditoolbox NDIToolbox requires NumPy, SciPy, rasterio (or GDAL), scikit-learn, matplotlib, and optionally xarray for multi-file datasets and dask for large-scale processing.
Basic workflow overview
A typical multispectral processing pipeline with NDIToolbox has the following stages:
- Data ingestion
- Preprocessing (radiometric/spectral corrections, geometric alignment)
- Index calculation and feature extraction
- Classification or regression analysis
- Post-processing and visualization
- Exporting results
Each stage has NDIToolbox functions or recommended patterns described below.
1) Data ingestion
NDIToolbox supports reading single and multi-band rasters while preserving geospatial metadata. Use the unified reader to obtain a NumPy array and an associated metadata dict.
Example pattern:
from nditoolbox.io import read_raster arr, meta = read_raster('landsat_B4_B3_B2.tif') # arr shape: (bands, rows, cols) If you have separate files per band, helper functions stack them into a single multi-band array and align geotransforms where necessary.
2) Preprocessing
Preprocessing often includes:
- Radiometric calibration (DN to radiance/reflectance)
- Atmospheric correction (simple dark-object subtraction or integration with third-party tools)
- Cloud/shadow masking
- Geometric resampling and reprojection
- Noise reduction and bad-pixel handling
NDIToolbox provides helpers like to_reflectance, dark_object_subtract, apply_mask, and resample.
Example: converting digital numbers (DN) to top-of-atmosphere reflectance:
from nditoolbox.preprocess import to_reflectance reflectance, meta = to_reflectance(arr, meta, sun_elevation=45.0, exoatmospheric_constants=...) For more accurate atmospheric correction, export to specialized tools (e.g., Py6S) or call external processors and bring corrected images back into the NDIToolbox flow.
3) Index computation and spectral features
Common indices are one-line calls. For example:
from nditoolbox.indices import ndvi, ndwi, savi ndvi_img = ndvi(reflectance[3], reflectance[4]) # example band indices ndwi_img = ndwi(reflectance[2], reflectance[4]) NDIToolbox also includes functions to compute spectral angle mapper (SAM), continuum removal, and band ratios. You can supply custom band math expressions with a small expression parser.
4) Dimensionality reduction and endmember extraction
To reduce redundancy or prepare for classification:
- PCA (Principal Component Analysis)
- MNF (Minimum Noise Fraction)
- ICA (Independent Component Analysis)
Example PCA usage:
from nditoolbox.decomposition import pca_transform pc_stack, pca_obj = pca_transform(reflectance, n_components=5) Endmember extraction methods (N-FINDR, PPI) help with spectral unmixing workflows. NDIToolbox provides both classic implementations and wrappers around scikit-learn components.
5) Classification and unmixing
For classification tasks, NDIToolbox offers:
- Unsupervised: k-means, Gaussian Mixture Models
- Supervised: wrappers for RandomForest, SVM via scikit-learn
- Spectral unmixing: linear unmixing given endmembers
Example supervised classification with Random Forest:
from nditoolbox.classify import RandomForestClassifierWrapper clf = RandomForestClassifierWrapper(n_estimators=100) clf.fit(X_train, y_train) # X_train: n_samples x n_features pred = clf.predict(X_test) There are convenience functions to sample training data interactively from imagery and to compute confusion matrices and accuracy metrics.
6) Visualization and export
Visualization utilities help create RGB composites, false-color images, and spectral plots:
from nditoolbox.visualize import show_rgb, plot_spectrum show_rgb(reflectance, bands=(3,2,1), stretch='hist') plot_spectrum(pixel_spectra) Export results as GeoTIFFs preserving geospatial metadata:
from nditoolbox.io import write_raster write_raster('ndvi.tif', ndvi_img, meta) Example end-to-end script
Below is a concise example pipeline: read, convert to reflectance, compute NDVI, classify with k-means, and export results.
from nditoolbox.io import read_raster, write_raster from nditoolbox.preprocess import to_reflectance from nditoolbox.indices import ndvi from nditoolbox.classify import kmeans_cluster # Read arr, meta = read_raster('sentinel_multiband.tif') # Convert to reflectance reflectance, meta = to_reflectance(arr, meta, sun_elevation=55.0) # Compute NDVI (assuming NIR band is 7 and Red is 3 in this dataset) ndvi_img = ndvi(reflectance[6], reflectance[2]) # Simple unsupervised classification class_map = kmeans_cluster(reflectance.reshape(reflectance.shape[0], -1).T, n_clusters=5) class_map = class_map.reshape(reflectance.shape[1], reflectance.shape[2]) # Export write_raster('ndvi.tif', ndvi_img, meta) write_raster('class_map.tif', class_map.astype('uint8'), meta) Use cases and examples
- Agriculture: crop-type classification, vegetation health monitoring (NDVI time series).
- Forestry: canopy density, burn severity mapping, biomass proxies.
- Water resources: water detection (NDWI), turbidity proxies, shoreline change.
- Urban studies: impervious surface mapping, heat-island proxies (using thermal bands where available).
- Environmental science: glacier monitoring, soil properties (with appropriate spectral bands), land-cover change detection.
Performance tips
- Use memory-mapped reads (rasterio or xarray with dask) for very large scenes.
- Process in blocks/windows to keep memory usage bounded.
- Use dask-backed arrays for parallel processing and to chain lazy operations.
- Precompute and cache intermediate results when iterating parameters (e.g., testing different thresholds).
Extending NDIToolbox
NDIToolbox is modular; you can add custom indices, classifiers, or I/O plugins. Typical extension points:
- Add new index functions in indices module.
- Wrap specialized external processors (Py6S, Fmask) with I/O helpers.
- Implement new decomposition or unmixing algorithms using scikit-learn API compatibility.
Troubleshooting common issues
- Mismatched geotransforms when stacking bands: reproject/resample to a common grid before stacking.
- NaNs and infinite values after radiometric scaling: mask invalid pixels before downstream ops.
- Slow I/O: use compressed cloud-optimized GeoTIFFs (COGs) and a local cache for repeated reads.
Further reading and resources
- NDIToolbox documentation and API reference (install and use locally or view online).
- Rasterio and GDAL for robust geospatial I/O.
- scikit-learn for advanced machine learning workflows.
- Domain-specific tools: Py6S for radiative transfer, SEN2COR/ACOLITE for atmospheric correction of Sentinel/optical imagery.
NDIToolbox streamlines many repetitive tasks in multispectral image processing while remaining flexible for advanced users. Whether you’re prototyping analyses or building production pipelines, it provides a practical bridge between raw remote sensing data and actionable maps and indices.
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