Feature Analysis And Interpretation Of Typical Object For High Resolution SAR Images On Urban Areas

In recent years, with the development of sensor technology, high-resolution (HR) radar remote sensing satellites were successfully launched continuously, marking the high-resolution radar data became a easily obtained datasource which only can be used for military purposes originally. This datasource not only expands the scope of remote sensing application, and also increases the observation scale and the speed of data updating. Researchers can study urban surface details by interpreting HR SAR image on more refined spatial scale and shorter time baseline for changes observed for the urban ecological environment assessment, disaster rapid response, urban planning and cadastral survey. At present, common interpreting method for radar image is based on feature classification and recognition through extracting feature characteristic pixel by pixel. According to our study, pixel-level image analysis method is less effective for high-resolution SAR images. In order to get the more desired results, object-level features need be considered.Because there are many high-height buildings at dense urban areas, multi-refraction cause SAR imaging mechanism is very complicated, so this paper studis the object-oriented image analysis (OBIA) method for HR SAR images on non-dense urban areas, focusing on object-level context features, multi-scale segmentation algorithm and interpretating methods while taking the sar imaging characteristics into account. Main research content and innovations are the followings:(1) Benefiting from the highlight layerover can be easily detected on HR SAR images, this paper proposes two kinds of object-level context features named LAI (Layerover Adjacent Intensity) and SDD (Shining point Distribute Density) respectively for large buildings and dense residential areas. The scatter diagram analysis shows that these two features bring better spatial clustering ability than pixel-level features (such as color and texturue). Therefore, we regard these two object-level context features as the main features for urban auto-interpreting in HR SAR images.(2) We propose a segmentation algorithm named eFNEA (Edge restricted Fractal Net Evolution Approach) which not only can adapt to the Speckle noise and weak edge on HR SAR images, and also has parallel, multi-scale and adaptive-parameters characteristics. Firstly, we analyze edge-shift problem which exposed when the famous optical multi-scale image segmentation algorithm (FNEA) processes radar image, and then, propose splitting the original segmentation into two steps to improve the affection and accuracy by merging the edge information to constraint the region topology while small scale level region merge. On the other hand, aiming at the massive memory resource demand of HR SAR image processing and restricted capability of processing huge image data with single compute, we analysis the eFNEA characteristics and propose a parallel task decomposition and merging strategy based on multi-level grid. In term of parameters setting, we propose the generalized heterogeneity rule, and use multi-scale features to adapt weight of color and shape, because eFNEA global weight parameters can not adapt to urban and suburban issues at the same time.(3) Focusing on the there characteristics of China airborne image, E-SAR airborne image and TerraSAR-X satellite images, this paper propose three different object-oriented interpretation methods to extract and classify buildings based on eFENA segmentation algorithm and object-level features. Experiment result confirms the applicable potential of object-oriented method which can get 86% around accuracy, and is significantly better than the pixel analysis method in these cases.(4) Finally, this paper discusses SAR remote sensing image processing mode on the emerging cloud computing environment and proposes a specific prototype system (OpenRS-Cloud) to show GUI, computing mode, technical method and running flow. We realize the above eFNEA algorithm on OpenRS-Cloud platform, and the experiment result shows that cloud computing as the new computing environment is an effective method to improve the efficiency of algorithm processing and algorithm development in remote sensing area.