## 4 Semantic Understanding of Object Layer
### Target Object Semantics  
The object layer semantics of the geographic scene are usually concentrated on the basic level of human cognition, which can be represented by many target objects (Figure 4). For the semantic understanding of the target object, it is necessary to use the target detection algorithm to clarify the types of each object. In the fields of computer vision and pattern recognition, many target detection algorithms have been developed, for instance: the threshold-based detection method [47], the template-based detection method [48], target detection based on Hough transform or Hough forest [49], target detection based on classifiers [50], etc. For the target detection of HSR remote sensing images, the method of target detection in the computer vision field is usually used for reference, and the research is carried out around the object of special interest, in particular, the artificial structures closely related to human activities, such as buildings [51], ports [52], airport runways [53], roads [54], warships [55] and so on. For artificial structures with obvious shape features, it is generally possible to directly use their unique shape features for detection, for instance, extracting straight lines to detect linear targets in images [56]. For complex targets, the corresponding models can be constructed; for instance, the \"Building\" target model can be constructed by texture, shape and SIFT features, and the \"Port\" target model can be constructed by combining the information of coastline, wharf and embankment [57].  
For the target detection of HSR remote sensing images, it is more challenging to detect objects with large image sizes and various details. Target detection of HSR remote sensing images is studied from different perspectives. A multi-layer SVM classifier is used to exclude non-target regions to improve the speed of target detection in high-resolution remote sensing images [58]. The large remote sensing image is divided into smaller blocks, the salient and synopsis features of each block are extracted, and the target detection is realized by classification [59]. Target detection is also accomplished by first segmenting HSR remote sensing images and then merging regions related to the target based on knowledge [60]. In addition, the successful application of visual selective attention to the target location in large-format remote sensing images, and the results show that the visual attention mechanism can quickly focus on the place where the object to be detected appears in the complex large image. These methods are all beneficial explorations in the target detection of HSR remote sensing images [61].  
The existing target detection methods have strong pertinence and lack universal and robust target detection models and algorithms. The motion characteristics of the target to be detected (such as ship wake, submarine track), the use of the shadow of the target in the image, the removal of the visible cloud cover and so on need to improve the target detection model with pertinence. To realize the practicality of target detection, it is necessary to establish a target detection model and a fast algorithm for multi-source data fusion.