Landslide Recognition by Deep Convolutional Neural Network and Change Detection

Wenzhong Shi, Min Zhang, Hongfei Ke, Xin Fang, Zhao Zhan, Shanxiong Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

It is a technological challenge to recognize landslides from remotely sensed (RS) images automatically and at high speeds, which is fundamentally important for preventing and controlling natural landslide hazards. Many methods have been developed, but there remains room for improvement for stable, higher accuracy, and high-speed landslide recognition for large areas with complex land cover. In this article, a novel integrated approach combining a deep convolutional neural network (CNN) and change detection is proposed for landslide recognition from RS images. Logically, it comprises the following four parts. First, a CNN for landslide recognition is built based on training data sets from RS images with historical landslides. Second, the object-oriented change detection CNN (CDCNN) with a fully connected conditional random field (CRF) is implemented based on the trained CNN. Third, the preliminary CDCNN is optimized by the proposed postprocessing methods. Finally, the results are further enhanced by a set of information extraction methods, including trail extraction, source point extraction, and attribute extraction. Furthermore, in the implementation of the proposed approach, image block processing and parallel processing strategies are adopted. As a result, the speed has been improved significantly, which is extremely important for RS images covering large areas. The effectiveness of the proposed approach has been examined using two landslide-prone sites, Lantau Island and Sharp Peak, Hong Kong, with a total area of more than 70 km2. Besides its high speed, the proposed approach has an accuracy exceeding 80%, and the experiments demonstrate its high practicability.

Original languageEnglish
Article number9173780
Pages (from-to)4654-4672
Number of pages19
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume59
Issue number6
DOIs
Publication statusPublished - Jun 2021

Keywords

  • Change detection
  • convolutional neural network (CNN)
  • landslide
  • remotely sensed (RS) images

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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