Abstract
The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, the fragmented distribution of urban land use types and the complex structure of urban forests bring about a variety of challenges for urban land use mapping and the extraction of urban forests. Based on the DCNN algorithm, this study proposes a novel object-based U-net-DenseNet-coupled network (OUDN) method to realize urban land use mapping and the accurate extraction of urban forests. The proposed OUDN has three parts: the first part involves the coupling of the improved U-net and DenseNet architectures; then, the network is trained according to the labeled data sets, and the land use information in the study area is classified; the final part fuses the object boundary information obtained by object-based multiresolution segmentation into the classification layer, and a voting method is applied to optimize the classification results. The results show that (1) the classification results of the OUDN algorithm are better than those of U-net and DenseNet, and the average classification accuracy is 92.9%, an increase in approximately 3%; (2) for the U-net-DenseNet-coupled network (UDN) and OUDN, the urban forest extraction accuracies are higher than those of U-net and DenseNet, and the OUDN effectively alleviates the classification error caused by the fragmentation of urban distribution by combining object-based multiresolution segmentation features, making the overall accuracy (OA) of urban land use classification and the extraction accuracy of urban forests superior to those of the UDN algorithm; (3) based on the Spe-Texture (the spectral features combined with the texture features), the OA of the OUDN in the extraction of urban land use categories can reach 93.8%, thereby the algorithm achieved the accurate discrimination of different land use types, especially urban forests (99.7%). Therefore, this study provides a reference for feature setting for the mapping of urban land use information from VHSR imagery.
Highlights
Urban land use mapping and the information extraction of urban forest resources are significant, yet challenging tasks in the field of remote sensing and have great value for urban environment monitoring, planning, and designing [1,2,3]
This paper proposed a novel object-based U-net-DenseNet-coupled network (OUDN) algorithm for the mapping of urban land use information from very high spatial resolution (VHSR) imagery, and the information of urban land use and urban forest resources was extracted accurately
The results showed that the overall accuracy (OA) of the U-net-DenseNet-coupled network (UDN) algorithm for urban land use classification was substantially higher than those of the U and D algorithms in terms of spectral features (Spe), Spe-Index, and Spe-Texture
Summary
Urban land use mapping and the information extraction of urban forest resources are significant, yet challenging tasks in the field of remote sensing and have great value for urban environment monitoring, planning, and designing [1,2,3]. Rich detailed information gives similar objects (such as building composed of different construction materials) strong heterogeneity in the spectral and structural properties [16], resulting in the phenomenon of “same object with different spectra”. Traditional statistical classification methods encounter these problems in the extraction of urban forests from VHSR remote sensing images. Urban forests with fragmented distributions are composed of scattered trees, street trees, and urban park forest vegetation This creates very large challenges for urban land use classification and the accurate mapping of urban forests [17]
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