AID Scene Classification on Remote Sensing Data from Google Earth, Leveraging Transfer Learning, and Interpretation of Predictions Using LIME and GradCAM.

This repository contains code (ipynb) for a scene classification model using transfer learning on the AID dataset (https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets), along with functionalities for explaining predictions using LIME (Local Interpretable Model-Agnostic Explanations) and GradCAM (Gradient-weighted Class Activation Mapping). Additionally, a user interface built with Gradio allows users to upload or paste images for classification and visual explanation.

Project Overview

• Model: A pre-trained convolutional neural network (CNN) is leveraged (transfer learning) for classifying AID scene images into 30 categories.
• Explainability: LIME and GradCAM are used to provide insights into the model's decision-making process for individual predictions.
• User Interface: Gradio offers a user-friendly interface for uploading/pasting images, receiving predictions with probabilities, labels, and visual explanations generated by LIME and GradCAM.

Dependencies

This project requires the following Python libraries:

• TensorFlow
• Keras
• LIME
• grad-cam
• Gradio

Installation:

pip install tensorflow keras lime grad-cam gradio

Usage

1. Install dependencies: (See above)

2. Prepare data:

   • Download the AID dataset from Kaggle (https://www.kaggle.com/datasets/jiayuanchengala/aid-scene-classification-datasets).
   • Place the downloaded data in a directory named data within the project folder.

3. Train the model and predict (Image Classifier):

   • Train the model yourself or we will upload the model/provide the drive link to the model, the processed data and its label very soon.
   • Partial Classification Model: 11_Class_Classification_Model.ipynb with 11 class labels.
   • Classification Model: Classification_Model.ipynb with 30 class labels.
   • Epoch-wise loss and accuracy is given below.

4. Explainers:

   • LIME: It perturbs the input data and fits it to a linear model to explain the predictions from the Image Classifier.
   • Grad-CAM: Uses Class Activation Mapping to provide a visual explanation about the features that's highly contributing for the prediction.
   • For Explainer codes refer : Explainer_and_its_Interface.ipynb

5. Interface:

   • Upload an image or paste an image URL in the provided field.
   • Click "Predict" to receive the model's prediction with probability, class label, and visual explanations using LIME and GradCAM.
   • For Interface codes refer : Explainer_and_its_Interface.ipynb
   • Interface preview is depicted below:

Interface Preview

Notes

• This readme provides a general overview. Refer to the code within the repository (.ipynb) for detailed implementation and configuration options.
• The pre-trained model and any additional data files might be stored separately. Modify the code to point to the correct locations.

This project offers a starting point for exploring transfer learning, explainable AI techniques, and building user interfaces for image classification and explanation tasks on the AID dataset(aerial image dataset).