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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:

  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.

  1. 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

  2. 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).