Land Cover Classification of Resources Survey Remote Sensing Images Based on Segmentation Model

Abstract

Land type survey is an important task of land resources survey and the basis of scientific management of land resources. With the increasingly prominent problems of population, resources, and environment, there is an urgent need for a fast and accurate classification method of large-scale land use and land cover based on remote sensing data. Traditional machine learning classification methods based on pixel classification achieved sufficient results and are widely used, such as maximum likelihood classification and random forests method. However, with the development of the novel technology of deep learning, in practical application, for multi-classified land resources, how to use the fast and effective classification method of low and medium resolution RS images needs further research. This paper takes the land resource classification of the Tonghe medium resolution RS dataset of the third land survey in China as an example to screen and compare traditional machine learning classification methods and semantic segmentation models FC-DenseNet56, GCN, BiSeNet, U-Net, DeepLabV3, AdapNet, and PSPNet, which aim to select the optimal feature extraction model. The results show that the classification accuracy of the U-Net model can reach 93.62%, which is more accurate and effective than traditional machine learning methods and other semantic segmentation models. It is suitable for multi-classification tasks of land cover resources in low and medium resolution RS images and shows a superior effect in practical application. Besides, the conclusion of this study can provide a demonstration for large-scale land cover resources investigation using low and medium resolution RS images.INDEX TERMS land use and land cover, semantic segmentation, multi-classification, deep learning, U-Net.

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

“…Moreso, several studies have implemented works for very high RS images. However, only few studies have focused on low and medium-resolution images [70,80,117,156]. As future insight, it is recommended to conduct more research using fast and efficient DL methods for low and medium resolution RS.…”

SABNet: Self-Attention Bilateral Network for Land Cover Classification

“…T HANKS to the rapid development of aerospace technology, it is easier to acquire high-resolution remote sensing images with more detailed land surface information, which can support finer classification and increase the diversity of remote sensing applications, but how to effectively acquire the information contained in high-resolution remote sensing images has become an urgent problem to be solved. Land cover classification as a decoding method has gradually become an important task in high-resolution remote sensing image processing, and has wide applications in land-use planning [1], [2], environmental monitoring and protection [3], [4],…”

Improving Land Use and Land Cover Information of Wunbaik Mangrove Area in Myanmar Using U-Net Model with Multisource Remote Sensing Datasets

“…Unlike traditional machine learning classification methods, deep learning models exhibit the unique ability to automatically extract features and learn complex patterns from input features, thereby alleviating the necessity for extensive variable training. Numerous studies have demonstrated the superior performance of deep learning models over traditional ML methods in remote sensing applications [22][23][24]. Among state-of-the-art models, the U-Net segmentation model and artificial neural networks (ANNs) have consistently demonstrated superior performance in remote sensing image analysis [25][26][27].…”