The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Land cover image segmentation based on individual class binary segmentation
Author:	Somasunder, Sathyanarayanan
View Online:	njit-etd2021-030 (ix, 22 pages ~ 2.8 MB pdf)
Department:	Department of Computer Science
Degree:	Master of Science
Program:	Computer Science
Document Type:	Thesis
Advisory Committee:	Shih, Frank Y. (Committee chair) Roshan, Usman W. (Committee member) Oria, Vincent (Committee member)
Date:	2021-05
Keywords:	Deep learning Image segmentation Remote sensing Convolutional neural network
Availability:	Unrestricted
Abstract:	Remote sensing techniques have been developed over the past decades to acquire data without being in contact of the target object or data source. Their application on land-cover image segmentation has attracted significant attention in recent years. With the help of satellites, scientists and researchers can collect and store high resolution image data that can be further processed, segmented, and classified. However, these research results have not yet been synthesized to provide coherent guidance on the effect of variant land-cover segmentation processes. In this paper, we present a novel model that augments segmentation using smaller networks to segment individual classes. The combined network trains on the same data but with the masks, combined and trained using categorical cross entropy. Experimental results show that the proposed method produces the highest mean IoU (Intersection of Union) as compared against several existing state-of-the-art models on the DeepGlobe dataset.