Multi-Class Image Classification Model for Land Use and Land Cover with Sentinel-2 Using TensorFlow Take 1

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Land Use and Land Cover with Sentinel-2 dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.

INTRODUCTION: This dataset is part of a research study that addresses the challenge of land use and land cover classification using Sentinel-2 satellite images. The research project presented a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes within a total of 27,000 labeled and geo-referenced images. The study project also provided benchmarks for this novel dataset with its spectral bands using deep Convolutional Neural Network (CNNs).

In this Take1 iteration, we will construct a CNN model using a simple three-block VGG architecture and test the model’s performance using a validation dataset (20%) set aside from the training images.

ANALYSIS: In this Take1 iteration, the baseline model’s performance achieved an accuracy score of 98.83% on the training dataset after 20 epochs. Furthermore, the final model achieved an accuracy score of 86.06% on the validation dataset.

CONCLUSION: In this iteration, the simple three-block VGG CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with more CNN architectures for further modeling.

Dataset Used: Land Use and Land Cover with Sentinel-2

Dataset ML Model: Multi-class image classification with numerical attributes

Dataset Reference:

A potential source of performance benchmarks:

The HTML formatted report can be found here on GitHub.