关键词: 深度学习, 土地覆盖分类, 高分辨率, 编码解码架构, 卷积神经网络, 自动分类, 残差网络, 多尺度损失函数

Abstract:

Land cover change refers to the areal change and type transformation between vegetation cover and non-vegetation cover caused by climate change and human activities, which is closely related to human survival and development, ecological environment evolution, and material energy cycle. Accurate classification is the basis of land cover change while land cover change is the core of global change research. The rapid development of high-resolution remote sensing technology poses a dual challenge to the speed and accuracy of land surface classification. In recent years, the development of new artificial intelligence technology has realized the automatic segmentation of natural scene images. Intelligent image processing technology has become an important force to promote the improvement of remote sensing information service level in the era of big data. The deep learning method represented by the convolution neural network also has significant advantages in the field of remote sensing image classification. To compare the impacts of deep learning model design on the classification results of high-resolution remote sensing images, this paper takes Gaofen-1 images of Zhengzhou City in Henan Province in 2019 as an example. This paper compares and studies the differences of four diverse deep learning network models, improved based on UNet model, in the application of automatic land cover classification of high-resolution images. Furthermore, this paper discusses the influence mechanism of encoder and decoder architecture settings, such as residual network, multi-scale loss function, skip-layer connection, and attention mechanism module, on classification accuracy. The results show that the MS-EfficientUNet model with multi-scale loss function, skip-layer connection, and attention mechanism module is the best for Zhengzhou City land cover classification, with an overall classification accuracy, based on pixel evaluation, of 0.7981. By introducing multi-scale loss function into the decoder, the classification accuracy of the forest, water, and other categories can be effectively improved. Moreover, by improving the encoder, adding skip-layer connection and attention mechanism, the classification accuracy of grassland, water, and other categories can be further improved. The results show that the powerful generalization ability of deep learning technology can effectively break through the spectral and time-phased differences between images, and realize the feature adaptive enhancement and intelligent decision-making, which has great potential in the field of high-resolution remote sensing image segmentation. However, further improvement of classification accuracy and multi-level and large-scale fine classification method are still the focus of the next step. At the same time, the unification of image sequence and the expansion of training samples are also the key factors to further improve the classification accuracy.

Key words: deep learning, land cover classification, high resolution, encode and decode architecture, convolutional neural network, automatic classification, residual network, multi scale loss function