FCD-R2U-net

Forest change detection in bi-temporal satellite images using the recurrent residual-based U-net

verfasst von

Ehsan Khankeshizadeh, Ali Mohammadzadeh, Armin Moghimi, Amin Mohsenifar

Abstract

Forest changes caused by fires, clear-cuts, and Land Use/Land Cover (LULC) changes have negatively affected the climate, wildlife, and global ecosystem. By monitoring the forest changes, managers and planners can make appropriate decisions to preserve these natural areas. In this regard, this paper presents a novel deep learning-based forest change detection method including two main steps: (1) producing a new difference image enabling a more efficient distinction of changed and unchanged areas, and (2) generating a reliable forest change map by applying the recurrent residual-based U-Net deep neural network (R2U-Net) on the difference image. The recently introduced forest fused difference image (FFDI) is first improved by modifying its weighted angular operator as well as applying the fast local laplacian filter (FLLF) to generate an enhanced forest fused difference image (EFFDI). R2U-Net is subsequently used to segment the EFFDI into the changed and unchanged regions because it preserves their geometric shapes more effectively than other U-net variants. To assess the efficacy of the presented method, experimental results were conducted on four bi-temporal images acquired by the Sentinel 2 and Landsat 8 satellite sensors. The qualitative and quantitative results demonstrated the effectiveness of the proposed EFFDI in reflecting the true forest changes from the background. Moreover, compared with the other conventional U-Net-based models, including U-Net, ResU-Net, and U-Net ++, forest changes and their geometrical details were better preserved by R2U-Net. Furthermore, the proposed approach outperformed the other state-of-the-art supervised and unsupervised change detection methods in terms of quantitative and qualitative results, demonstrating its high potential for forest change detection applications.

Externe Organisation(en)

K.N. Toosi University of Technology

Typ

Artikel

Journal

Earth science informatics

Band

15

Seiten

2335-2347

Anzahl der Seiten

13

ISSN

1865-0473

Publikationsdatum

12.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Erdkunde und Planetologie (insg.)

Ziele für nachhaltige Entwicklung

SDG 13 – Klimaschutzmaßnahmen, SDG 15 – Lebensraum Land

Elektronische Version(en)

https://doi.org/10.1007/s12145-022-00885-6 (Zugang: Geschlossen)