Document Type

Thesis

Date of Award

5-31-2021

Degree Name

Master of Science in Computer Science - (M.S.)

Department

Computer Science

First Advisor

Frank Y. Shih

Second Advisor

Usman W. Roshan

Third Advisor

Vincent Oria

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.

Share

COinS