Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Comparison of Multi-Class and Fusion of Single-Class SegNet Model for Classifying Karst Wetland Vegetation Using UAV Images

Version 1 : Received: 17 February 2021 / Approved: 19 February 2021 / Online: 19 February 2021 (09:44:24 CET)

How to cite: Fu, B.; Liu, M.; He, H.; Fan, D.; Liu, L.; Huang, L.; Gao, E. Comparison of Multi-Class and Fusion of Single-Class SegNet Model for Classifying Karst Wetland Vegetation Using UAV Images. Preprints 2021, 2021020426 (doi: 10.20944/preprints202102.0426.v1). Fu, B.; Liu, M.; He, H.; Fan, D.; Liu, L.; Huang, L.; Gao, E. Comparison of Multi-Class and Fusion of Single-Class SegNet Model for Classifying Karst Wetland Vegetation Using UAV Images. Preprints 2021, 2021020426 (doi: 10.20944/preprints202102.0426.v1).

Abstract

Karst wetlands are being seriously damaged, and protecting it has become an important matter. Karst vegetation is the essential component of wetland and plays an important role in in the ecological functions of wetland ecosystems. Classifying karst vegetation is important for karst wetlands protection and management. This paper addressed to classify karst vegetation in Huixian National Wetland Park, located in China using the improved SegNet Deep-Learning Algorithm and UAV images. This study proposed a method to fuse single-class SegNet models using the maximum probability algorithm for karst vegetation classification, and compared with object-based RF classification and multi-class SegNet classification, respectively. This paper evaluated the performance of multi-class SegNet model and fusion of single-class SegNet model with different EPOCH values for mapping karst vegetation. A new optimized post-classification algorithm was proposed to eliminate the stitching traces caused by SegNet model prediction. The specific conclusions of this paper include the followings:(1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall classification accuracy (87.34%); (2) The optimized post-classification algorithm was able to improve prediction accuracy of SegNet model, and it could eliminate splicing traces; (3) The karst wetland vegetation classifications produced by single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy(F1-Score) between 10%~25%;(4)The EPCOH values and textural feature important impact on karst wetland vegetation classifications. The SegNet model with EPCOH 15 achieved greater classification accuracy(F1-Score) than the model with EPOCH 5 or 10. The textural feature improved improves the capability of the SegNet model for mapping karst vegetation;(5) Fusion of single-class SegNet models and object-based RF model could provide high classifications results for karst wetland vegetation, and both achieved greater 87% overall accuracy.

Subject Areas

karst wetland mapping; SegNet model; UAV images; fusion model; texture feature

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