Evaluating Performance of Deep Neural Networks for Crop Classification and Complex Facility Recognition

Automatic identification of different crops purely using satellite imagery is an active area of research which helps the government in accurately estimating the yields of different crops, thereby helping them prepare proper action plans to ensure that the supply of food matches the demand. With rapid rise in the world population, this is an essential factor. On the other hand, detecting different types of complex structures such as coal plants, gas plants, nuclear power plants and airports is another emerging area of research. Detecting nuclear power plants is essential from a national security perspective, for monitoring nuclear proliferation. With the availability of powerful computing resources, deep neural networks have increasingly become the de facto standard in several image classification challenges. In this paper, we evaluate the performance of several well-known deep neural networks (Convolutional Neural Networks) in performing the aforementioned two tasks - crop classification and complex facility recognition. Based on our experiments, we will evaluate how state-of-the-art networks will perform in performing tasks such as crop classification and complex facility recognition.