Land Cover Image Segmentation Based on Individual Class Binary Masks

Sathyanarayanan Somasunder, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review

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 during recent years. With the help of satellites, scientists and researchers can collect and store high-resolution image data that can be further be 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 is trained 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.

Original languageEnglish (US)
Article number2154034
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • convolutional neural network
  • Deep learning
  • image segmentation
  • remote sensing

Fingerprint

Dive into the research topics of 'Land Cover Image Segmentation Based on Individual Class Binary Masks'. Together they form a unique fingerprint.

Cite this