Knowledge And Data Driven Intelligent Recognition Model For Typical Ground Objects From Remote Sensing Images

With the rapid development of remote sensing technology,extracting features and recognizing ground objects from high-resolution remote sensing images(HRSIs)are of important application significance in many areas such as urban planning and environmental monitoring.In this thesis,intelligent ground object recognition methods focus on object segmentation and change detection.Due to the complexity and diversity of ground object features,traditional methods like manual visual interpretation and feature engineering-aided machine learning methods,cannot meet the high requirements on intelligent,accurate and reliable recognition.In addition,the traditional methods heavily depend on professional knowledge,resulting in weak generalization capability.Recently,the explosive development of deep learning provides new intelligent methods for remote sensing image processing,and convolutional neural network(CNN)has achieved state of the art(SOTA)in computer vision and thus been widely employed in ground object recognition from HRSIs.In this thesis,hybrid knowledge-and data-driven semantic segmentation-based deep learning methods are studied to enable accurate ground object recognition,especially on the tasks of road extraction and semantic change detection.The main contributions of this thesis include:1.To enhance the recognition of small-size roads in complex environments,where vegetation shadow and many kinds of environmental interference with similar texture and shape exist,a semantic segmentation model termed DDU-Net is developed,which holds dual decoder structure and integrates dilated convolution and attention mechanism.The proposed DDU-Net model captures multi-scale context information by a dilated convolutions cascading parallel.Also,the global average pooling branch is added to fuse the global information,and the channel-spatial dual attention mechanism is introduced to improve the characterization ability of features.Furthermore,the dual decoder structure can enhance the restoration of detail information.Sufficient experiments are carried out on the open Massachusetts road dataset,and the experimental results show that the DDU-Net model can greatly improve the robustness of road extraction when roads either have similar color to the background or are shaded by vegetation shadow.Compared with mainstream baseline models,DDUNet can effectively improve the accuracy of road extraction and the connectivity of segmentation.Moreover,ablation experiments and feature visualization show that the introduced dilated convolution attention module and dual decoder structure can effectively capture global context information and extract detailed features at the same time.2.In order to locate the change areas of a remote sensing image accurately and identify the categories of ground objects reliably before and after the change,under the principle of multi-task learning,a semantic change detection model with siamese network structure and attention mechanism is developed.In this model,the semantic change detection problem is translated into the combination of binary-classification change detection task and multi-classification ground object segmentation task.Siamese network structure is employed to extract the features of bitemporal images,which are shared by the two tasks to reduce training complexity.The prediction output of the change detection task feeds the ground object segmentation task,and then fuses with the bi-temporal image features at the feature level so as to retain more context information.Attention mechanism is introduced to promote the generalization and robustness of the model.Both ablation and comparative experimental results on the land change dataset show that the proposed model can improve the accuracy of semantic change detection,especially for smallscale objects.