High-Resolution Satellite Image Classification Using Lightweight CNN with Depth wise Separable Convolutions.

📌 Introduction

This repository contains the implementation of multiple state-of-the-art deep learning architectures for crop disease classification using high-resolution satellite and macro-scale agricultural imagery. The models are designed to optimize computational efficiency while maintaining high classification accuracy, achieving an average accuracy of 95.7% across all models.

🚀 Models Implemented

The following architectures have been fine-tuned and optimized to enhance disease classification performance:

• DenseNet121 (DenseNet121_model.ipynb): Efficient feature propagation and reduced computational overhead.

• NASNetALarge (NASNetALarge_model.ipynb): Reinforced learning-based architecture with adaptive feature recalibration.

• SENet (SENet_model.ipynb): Squeeze-and-Excitation blocks for dynamic channel-wise feature enhancement.

• SE-ResNeXt50 (Se_ResNeXt50_model.ipynb): Multi-branch feature extraction with residual learning and attention mechanisms.

• TinyNet (TinyNet_model.ipynb): Lightweight yet powerful convolutional model for real-time inference.

📊 Key Features & Enhancements

• Adaptive Feature Recalibration: Models incorporate channel and spatial attention to refine feature extraction.

• Progressive Resizing & Test-Time Augmentation (TTA): Enhances model robustness and generalization across different image scales.

• Self-Distillation & Knowledge Transfer: Improves feature learning and optimizes the performance of smaller architectures.

• Class-wise Dataset Partitioning: Balanced dataset representation to mitigate bias and improve performance consistency.

📈 Performance Metrics

Each model has been evaluated using rigorous performance metrics:

• Accuracy: Average 95.7% across all models.

• ROC-AUC Curve: Comprehensive evaluation of classification thresholds.

• Precision-Recall Analysis: Ensures robustness against class imbalance.

• Confusion Matrix Visualization: Provides insights into misclassifications.

Train-Test-Validation split between classes

📬 Contact

For queries, feel free to reach out:

GitHub: ArnavGhosh999

Email: [email protected]