Hyperspectral Image Classification Using Convolutional Neural Networks
May 15, 2024
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1 min read
In this project, I developed a Convolutional Neural Network (CNN) model tailored for hyperspectral image classification. Hyperspectral imaging captures a wide spectrum of light per pixel, providing detailed information about the materials within a scene. Traditional classification methods often struggle with the high dimensionality and complexity of hyperspectral data. To address these challenges, I implemented a CNN that effectively learns both spectral and spatial features, improving classification performance.
Key Features:
- Spectral-Spatial Feature Extraction: The CNN model simultaneously processes spectral and spatial information, capturing intricate patterns within hyperspectral data.
- Dimensionality Reduction: Incorporated techniques such as Principal Component Analysis (PCA) to reduce data dimensionality, mitigating the “curse of dimensionality” and enhancing computational efficiency.
- Robust Classification: The model demonstrates high accuracy in distinguishing between various land cover types, minerals, and vegetation species, even in complex environments.
