Analysis Of Multi-temporal Remote Sensing Imagery By Coupling Land Surface Change Pattern And Multi-dimensional Characteristics

Change detection is a process of determining changes of the same object or natural phenomenon through observations at different time.In recent years,with the increase of human activities,the land surface has undergone significant changes.Remote sensing earth observation technology can obtain land cover information of a large region in a short time,providing an effective way for periodic collection and dynamic monitoring of large area information.It has been widely used in exploring land surface changes caused by natural factors(e.g.,fires,earthquakes,locust plagues.)and human activities(e.g.,deforestation,farming and planting,urbanization.).Therefore,effectively discovering and describing the changes in multi-temporal remote sensing images is a research hotspot and frontier issue in remote sensing science and technology.With the accumulation of archived data and the emergence of more advanced sensors,remote sensing images can provide more detailed change information.However,there are still many problems in the methodology and application of change detection based on these images.First,the current single-level feature extraction and analysis methods cannot fully characterize the change of spatial information in images.The spatial patterns of land surface change is not fully considered in change detection process.Secondly,the exploration and utilization of temporal information among multitemporal images is obviously insufficient.Third,for time series change detection tasks,the reliability of the results based on existing time series model is poor,whose stability in complex land surface change scenarios needs to be improved.Fourth,the interpretability in spatio-temporal characteristics and change patterns of land surface elements in the current time series change detection needs to be enhanced.There is a lack of in-depth discussion on the interaction and influence of multi-element changes in a complex environment.In order to overcome the aforementioned problems,according to the characteristics of different types of remote sensing datasets,the spatial and temporal image features at different levels and scales were excavated under the guidance of the spatial and temporal patterns of land surface changes.Three change detection approaches were proposed in the thesis,including bi-temporal change detection approach with consideration of spatial pattern,tri-temporal change detection approach with consideration of temporal pattern,and time series change detection and analysis approach with consideration of the pattern of multi-element interaction.The main research work and conclusions of the thesis are as follows:(1)According to the characteristics of similarity,heterogeneity and multiscale in the spatial distribution of land surface composition and change,a change detection approach based on multi-level spatial information integration was proposed.The spatial patterns of real ground objects can be expressed in different levels of spatial features in remote sensing images.The change information between remote sensing images can be comprehensively described from different angles by combining pixel-level,neighborhood-level,object-level and scene-level spatial features.However,the problems such as high correlation of multi-level features,low separability of complex change types,and large demand for training samples arose.Therefore,feature selection was carried out to find the most effective feature combination to reduce the feature dimension and information redundancy.The iterative active learning strategy was also adopted to search for new valuable training samples step by step,which continuously optimized the models of feature extraction and change detector.The results showed that the change detection method combined with multilevel spatial features fully considered the spatial patterns of land surface changes.It significantly improved the accuracy and efficiency of change detection results and effectively reduced change detection cost.(2)The logical relationship between land surface changes in multi-temporal images was explored,and a change detection approach with consideration of the logical rules in temporal space was proposed.According to the logical relationship among land surface changes in tri-temporal images,bi-temporal change detection results can be mutually verified,and a logical system of tri-temporal land surface change pattern was constructed.The incorrect patterns were identified by reasoning the type of pattern,and the anomalous results were modified through the reliability comparison among bi-temporal change detection results.The logical relationship in temporal space was utilized to make up for the insufficiency of spectral and spatial information,improving the understanding of the process of land surface change.The results showed that the change detection approach that took the logical relationship of the land surface changes into account made full use of the temporal patterns among images to modify the anomalous outcomes from original bi-temporal change detection,effectively improving the accuracy and reliability of change detection results.(3)A time series change analysis approach based on ensemble learning strategy was proposed,realizing the multi-element interaction analysis of land cover change and vegetation phenology response in time series images.The ability of a single classifier in discriminating unstable time series model was limited,leading to low reliability of the results.Integrating different base classifiers gave full play to the advantages of each classifier,improving the performance of discriminating time series land cover changes.Meanwhile,in view of the lack of consideration of land cover changes in the current long-term vegetation phenology analysis,accurate spatiotemporal analysis of vegetation phenology in different regions was realized by integrating the continuous land cover change results.The results showed that the timeseries change detection approach based on the integration of multiple classifiers significantly improved the accuracy of the continuous land cover change detection.Moreover,the interaction among land surface elements was taken into account in the analysis of long-term land cover changes.The response results of the spatio-temporal differentiation and change patterns of vegetation phenology under the condition of urbanization were analyzed.The direct and indirect influences of land cover dynamic changes on vegetation phenology were revealed.