English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Landslide inventory mapping (LIM) is an important prerequisite for disaster emergency rescue and landslide sensitivity analysis. It has been proven that convolutional neural networks have better performance for LIM than traditional machine learning methods such as support vector machines (SVM), and random forests (RF). However, the accuracy of existing methods based only on optical images is low due to the complex landslide background. Moreover, the multi-scale features of landslides are not considered in convolutional neural network methods. Therefore, this study proposes Multi-featured independent component analysis UNet3&#x002B; (MICUNet3&#x002B;) for landslide inventory mapping based on optical images, which combines co-feature, independent component analysis (ICA), and UNet3&#x002B;. Firstly, normalized difference vegetation index (NDVI) and gray level co-occurrence matrix (GLCM) are extracted from remote sensing images acquired pre- and post- earthquake event and then processed by change vector analysis. Then, ICA is implemented for NDVI, GLCM, and three elevation factors. Lastly, the three principal components and the post-event images are fed into UNet3&#x002B; to generate LIM by multi-scale features and deep supervision. Finally, we validate the proposed method by using the co-seismic landslide of Jiuzhaigou earthquake as an experiment. The results show that the performance of recall, F1-score and mIoU are 0.13, 0.22 and 0.11 higher than those of the post-event-only images, respectively, indicating that the proposed method can effectively solve the problems of landslide identification in terms of multi-scale features and complex background.

Landslide Inventory Mapping Based on Independent Component Analysis and UNet3+: a Case of Jiuzhaigou, China