Research On Change Detection Method Of Multitemporal Multispectral Remote Sensing Images Based On Information Guidance

With the advancement of sensor technology and aerospace detection capability,earth observation applications based on remote sensing data are gradually changing toward dynamization,refinement and quantification.Multitemporal remote sensing image change detection has received wide attention as a key technology in dynamization,and has shown important application values in the fields of urban area expansion analysis,hot spot area tracking and monitoring,natural resources investigation and exploration,and military defense.The goal of this technique is to interpret the changes among multitemporal remote sensing images by jointly analyzing the spatio-temporal spectral characteristics,which usually includes binary change detection and multiple change detection.At present,the temporal-spatial-spectral characterization methods mainly adopt two typical ways: separate processing of each temporal image and fusion or stacking processing of multitemporal image,because the former does not model the correlation between multitemporal images,and the latter may suffer from feature uncertainty and information loss,therefore,this paper aims to break through the bottleneck of comprehensive modeling and joint utilization of spatio-temporal spectral characteristics,based on multitemporal multispectral remote sensing images,and proposes an information-guided processing way with feature spectra,subspace statistical properties and change information as guides,respectively,which improves the effectiveness and adaptability of the algorithm under realistic conditions such as complex feature background and spatio-temporal spectral variability by mutual constraints of intertemporal information.The main contents include the following parts.The paper firstly describes the basic techniques of multitemporal image change detection,and introduces the traditional methods of change detection for low and medium spatial resolution multispectral images and high spatial resolution multispectral images from different information attributes of multispectral images,which are used to deeply understand the two typical analysis ways of spatio-temporal spectral characteristics:separate processing of each temporal image and fusion or stacking processing of multitemporal images.Based on this,a method combining depth-parallel structure and self-attention is proposed,and the above methods are experimentally validated using real data.The results reflect the advantages and disadvantages of traditional processing ways while providing theoretical and experimental support for the design of subsequent methods.A change detection method based on the feature spectral information guidance and structure assistance is proposed to overcome the problem that the complex temporal spectral variation conditions in low and medium spatial resolution multispectral images lead to the drastic perturbation of the unchanged regions properties,making it difficult to characterize the change properties effectively.Since low and medium spatial resolution images are relatively rich in spectral information,differences in radiation brought about by imaging conditions and time have a large impact on the stability of the spectral properties of unchanged regions,making it difficult to capture the change features.By introducing the feature spectral information extraction and perturbation spectral information modeling methods,the paper adopts the spectral information guided constraint to project the multitemporal images into the space tensed by the feature spectra,and uses the global structure information to enhance the projected features to alleviate the phenomena of inconspicuous edges,poor integrity and more scattered false alarms.The paper achieves better detection results on three groups of multispectral images,proving the advantages of the proposed method.A change detection method based on subspace information guidance and neighborhood assistance is designed to address the problems of difficult spatio-temporal characterization and low validity of change information extraction caused by complex data distribution characteristics and relative lack of identifiable spectral information under high-resolution imaging conditions.Due to the limitation of multispectral imaging equipment in resolution,relatively little identifiable spectral information and complex data distribution characteristics in high spatial resolution images make it more difficult to effectively model the characteristics of change regions.The paper establishes an information guidance mechanism by combining subspace analysis methods and Gaussian mixture model modeling way,using subspatial information such as the statistical distribution of the spatial-spectrum and the parameters of the factorization matrix to project multitemporal images into the data subspace.Meanwhile,the paper uses the neighborhood assistance method in physical space to alleviate the phenomenon of inconspicuous change boundaries and more scattered false alarms.The experimental results on several group of data show that the proposed method can effectively achieve the change detection of high-resolution multispectral images,and high detection accuracy is also obtained in the comparison experiments.A multiple change detection method based on change information guidance and spatial-spectral association is proposed on the basis of change detection for the problem that change features in different categories are difficult to distinguish effectively due to the limited number of spectral bands in multispectral images,the multi-scale characteristics of features in complex backgrounds and the obvious differences in the same feature characteristics.Since the spectral properties can improve the feature recognition ability and the spatial information of images can be decomposed into structural components related to the properties of feature categories at specific scales,the paper uses nonlinear operations and optimization strategies to express the spatial spectral properties of multitemporal images by introducing a spectral bands generation method and a joint constrained convolutional sparse representation method,and then highlights the properties of multiple changes.At the same time,the change detection results obtained by using the information-guided approach are introduced into the above-mentioned spatial spectral properties to assist in change localization.The experimental results of multiple datasets show that the proposed method can achieve effective multiple change detection.