Comparison of multi-class and fusion of multiple single-class SegNet model for mapping karst wetland vegetation using UAV images

Abstract

Wetland vegetation classification using deep learning algorithm and unmanned aerial vehicle (UAV) images have attracted increased attentions. However, there exist several challenges in mapping karst wetland vegetation due to its fragmentation, intersection, and high heterogeneity of vegetation patches. This study proposed a novel approach to classify karst vegetation in Huixian National Wetland Park, the largest karst wetland in China by fusing single-class SegNet classification using the maximum probability algorithm. A new optimized post-classification algorithm was developed to eliminate the stitching traces caused by SegNet model prediction. This paper evaluated the effect of multi-class and fusion of multiple single-class SegNet models with different EPOCH values on mapping karst vegetation using UAV images. Finally, this paper carried out a comparison of classification accuracies between object-based Random Forest (RF) and fusion of single-class SegNet models. 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 accuracy of 87.34%; (2) the optimized post-classification algorithm improved classification accuracy of SegNet model by eliminating splicing traces; (3) classification performance of single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy (F1-Score) ranging from 10 to 25%; (4) Fusion of single-class SegNet models and object-based RF classifier both produced good classifications for karst wetland vegetation, and achieved over 87% overall accuracy.

Ephemeral gully development in the hilly and gully region of China's loess plateau

“…Its core component is an encoder network and corresponding decoder network, followed by a pixel‐level classifier, which outputs the probability map of the K channel, where K is the number of classification categories. Compared with other models, SegNet has fewer training parameters, smaller memory occupation, and shorter network training time (Deng et al, 2022; Jiang et al, 2020; Manickam et al, 2020). A comparison of accuracy evaluation results using six indexes (Accuracy, Precision, Recall, F1 value (weights both precision and recall), receiver operating characteristic (ROC) curve, and area under ROC curve (AUC)) revealed that SegNet ranked first for ephemeral gully recognition in the hilly and gully region of the Loess Plateau, followed by Recurrent residual U‐Net (R2U‐Net) and U‐Net (Liu et al, 2022).…”

Economic Fruit Forest Classification Based on Improved U-Net Model in UAV Multispectral Imagery

“…The improved U-Net model was named ISDU-Net, and ISDU-Net was compared with the FCN, SegNet, and U-Net models to analyze the qualitative and quantitative results [51][52][53][54][55][56][57][58][59][60][61][62].…”

Segmenting Live Cattle using a New Approach to Combine Superpixels and SegNets

“…As Chen et al [5] show, the task of segmenting livestock animals such as cows and pigs in images has accompanied the rise of deep learning over traditional computer vision techniques. In general, deep learning has become extensively used for semantic segmentation, with SegNets and its derivations being one of the most cited deep networks to this end [6]- [8]. One weakness of SegNets, however, is that they usually fail to correctly classify pixels that are near object edges, therefore producing sub-optimal outlines [9].…”