A Prior Semantic Network for Large-Scale Landcover Change of Landsat Imagery

Xuan Yang, Yongqing Bai, Pan Chen, Cong Li, Kaixuan Lu and Zhengchao Chen ()
Additional contact information
Xuan Yang: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yongqing Bai: Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Pan Chen: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Cong Li: Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Kaixuan Lu: Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Zhengchao Chen: Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Sustainability, 2022, vol. 14, issue 20, 1-38

Abstract: Landcover change can reflect changes in the natural environment and the impact of human activities. Remotely sensed big data with large-scale and multi-temporal key characteristics provide the data support for landcover change information extraction. The development of deep learning provides technical method support for information extraction from remotely sensed big data. However, the current mainstream deep learning change detection methods only establish the changing relationship between two phases of images. They cannot directly extract the ground object categories before and after the change. It is easily affected by pseudo-changes caused by the color difference of multi-temporal images, resulting in many false detections. In this paper, we propose a prior semantic network and a difference enhancement block module to establish prior guidance and constraints on changing features to solve the pseudo-change problem. We propose a semantic-change integrated single-task network, which can simultaneously extract multi-temporal landcover classification and landcover change. On the self-made, large-scale multi-temporal Landsat dataset, we have performed multi-temporal landcover change information extraction, reaching an overall accuracy of 83.1% and achieving state-of-the-art performance. Finally, we thoroughly analyzed the landcover change results in the study area from 2005 to 2020.

Keywords: landcover change; deep learning; prior constraint; difference enhancement; single-task network; Landsat; multi-temporal (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/13167/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/13167/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:20:p:13167-:d:941708

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().