Research On Image Understanding Of Building Regions In Remote Sensing Images

Asoneofthegreatachievementsofhuman’smodernscienceandtechnology, remotesensing has been playing a very important role during the decision-making of industry,agriculture, politics, economy and military, et al. With the launching of high-resolutionremote sensing satellites, remote sensing images is increasing with the speed of TB leveleveryday. To extract useful information from the trillions of remote sensing images withconventional, manual interpretation method, is a mission impossible. Therefor, the re-search of automatically or semi-automatic interpreting remote sensing images becomesa hot topic. Image understanding, which trying to interpret the scene and the objects inthe scene to some meaningful, understandable entities, happens to hold the same viewwith remote sensing image interpretation. In this thesis, some remote sensing image, es-pecially high-resolution remote sensing image oriented topics are studied with the imageunderstanding theory and methods, such as: building area segmentation, building objectsdetection&extraction,buildingrecognition,buildingareaclassificationandbuildingareaunderstanding, etal. Thegoalistorecognizethebuildingsandunderstandthescenescon-structed by the buildings. The contributions including:After Elaborating the importance of context in image segmentation and discussingthe ability of using context of CRF, by modifying the potential function of CRF, an im-proved building area segmentation oriented CRF is proposed. As CRF has the advantageof fusion multi-features to segment, after analyze the characteristics of building area inremote sensing images, we propose to introduce the multi-scale texture features and the”in scale”, together with the”between scale” gradient features into CRF, to perform abetter segmentation.the proposed method has the ability of using the context in the labelimages, also, it can make good use of various context in the observed images. And withthe chosen features, the proposed method has a better segmentation result.In the section of building objects detection and extraction, A novel variational levelset model for multiple-building extraction from a single remote image which can generateclosed curves, is proposed. The object extraction could be fail due to the lost of low-levelinformation, in this thesis, we proposed to solve the problem by construct a buildings’prior-shape database, and consider multi-competing shapes together with the level setmodel. The curve evolution is constrained by the prior shape knowledge and the label function which dynamically indicates the region with which the prior shape should becompared. The building extraction is addressed through a level set image segmentationapproach that involves the use of the label function as well as the prior shape knowledge.The introduction of prior-shapes can guarantee that the extracted objects are meaningfullogical entities. In addition, the proposed model permits translation, scaling, and rotationof the prior shapes.Representation and description of the objects is the basis for object recognition. Inthis section, a local feature description algorithm for building objects, especially typicalsensitive objects, which is called the normalized pixel distribution histogram local de-scriptor (NPDHLD), is proposed. With the edge extracted by the method discussed in lastsection, a’log-polar’ coordinate is established by using every edge points as the coordi-nate origin. Normalize every pixel value, the local descriptor is constructed by capturingthedistributionoftheobjectedgepixelpointswhicharesituatedbeyondthecurrentoriginpoint. The objects are described with the proposed local description algorithm to build aobjectsfeaturedatabase. thelocalfeaturesextractingfromtheobjectstoberecognizedarematched with the ones in the object database under a’two-step matching’ strategy. Objectrecognition is completed after matching. The’two-step matching’ strategy improved thematching result, also reduced the computational complexity. The proposed recognitionmethod has a better result than SIFT under the same context.A kernel function—Hierarchical Log-Polar Matching Kernel which making use ofthe feature spatial in-formation, is proposed for building classification in remote sensingimages in this section. Image local features are extracted at first, and then traditionalclustering methods are used to quantize all feature vectors into several different types.Partition-ing the image into multi-level increasingly fine log-polar“sub-regions (bins)”. Bycomputinghistogramsoflocalfeaturesfoundinsideeachsub-regionineachlevel,theweighted multi-scale histograms is formulated, sum all weighted multi-level histogramsof each feature vectors, the final hierarchical log-polar kernel is established. The buildingclassification is done with a SVM trained using the“one-versus-all”rule. There is noexplicit object model in the proposed method, but represent the image by the overall con-text. Meanwhile, the proposed method, as mentioned before, take advantage of the spacerelationship between features which is ignored by conventional bag-of-feature methods,therefor, has a better classify result. Inordertounderstandthescenepresentedbythebuildingarea,abuildingareaunder-standing oriented semantic bayes network(SBN) which based on the city entities’ spaceconfiguration and semantic relationship, is proposed. After summarize the concept, com-position, and space configuration of the building entities class, as well as the city entityarea, the local semantic and space configuration of building entities are described undera unified probabilistic framework. The local semantic information of city entity area isexpressed by the building entity occurrence probability in the very city entity area; Thespace configuration of city entity area is expressed approximatively by verify the’Repre-sentative building entity class’ and their neighbors; The structure and parameters of SBNisderivedbydomainknowledgeandtrainingimages, andclassifythetestimageswiththeinference of the SBN, in other words, the understanding of the city entity area is achievedby the class probability of the city entity area. The experimental results shows that theproposed method has a better performance than traditional area classification methods.