Deep Learning-Based Ship Detection in Remote Sensing Imagery Using TensorFlow

Ship detection has been a very significant proposal that is recently being researched extensively due to its very wide application and help. Ship detection is the one which helps in handling several issues that endangers the ocean security based on of several grounds. Ship detection is included in the object segmentation and detection problem that requires the aid of computer vision to preprocess and process the satellite images. The whole process is treated differently by humans and computers which makes the process complex. It is because the later treats the images as number matrix. Hence, this paper tries to propose steps to solve the issue using deep learning and convolutional neural network that works toward achieving excellent accuracy as compared to the state-of-the-art method. This not only helps us detect an object in the homogenous background but also aids us immensely in object detection and processing among the heterogeneous background. This paper has achieved this using the Keras model which is a good accurate satellite image analysis tool. It can precisely detect the ships in the mid-seas. Datasets are collected from the Kaggle Ship Detection Challenge. Here we take the dataset which consists of image chips extracted from Planet satellite imagery collected over the San Francisco Bay area. This analysis would help us control the exploitation of the sea’s resources. The need of guaranteeing sustainable sea exploitation with the likes of fishing, coral extractions, artifact explorations is essential.