Study on transfer learning ability for classifying marsh vegetation with multi-sensor images using DeepLabV3+ and HRNet deep learning algorithms

作者：Man Liu, Bolin Fu, Donglin Fan, Pingping Zuo, Shuyu Xie, Hongchang He, Lilong Liu, Liangke Huang, Ertao Gao, Min Zhao · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2021 · DOI：10.1016/j.jag.2021.102531 · 被引用次数：44 · 研究领域：Advanced Image Fusion Techniques、Remote-Sensing Image Classification、Remote Sensing in Agriculture

The verification of the transfer learning ability of the convolutional neural network in the classification of natural vegetation is relatively lacking. In this paper, 16 combination scenarios of multispectral images in marsh vegetation were constructed. The influence of the combination with different spatial resolution gradients and spectral dimensions on the classification accuracy of marsh vegetation was systematically studied. Multi-sensor images were used to evaluate the transfer learning ability of DeepLabV3+ and HRNet algorithms in marsh vegetation, and analyse the transfer learning effect of two algorithms in different spatial resolution and spectral ranges. The majority voting method was used to fuse the classifications of high, medium and low spatial resolution images. Based on the largest area method, the fusion results were integrated with multi-scale segmentation to explore the classification ability of the integration of pixel-based classification and object-based classification. The average accuracies of different spatial resolutions to vegetation in multispectral images were statistically analysed in order to quantitatively study the classification ability of spatial resolution to marsh vegetation. The results indicated that: (1) image combination improved the classification accuracy of marsh vegetation in low-resolution images, and decreased the classification accuracy of vegetation in high- and medium-resolution images based on DeepLabV3+ and HRNet algorithm...