Abstract
Effectively identifying an airport from satellite and aerial imagery is a challenging task. Traditional methods mainly focus on the use of multiple features for the detection of runways and some also adapt knowledge of airports, but the results are unsatisfactory and the usage limited. A new method is proposed to recognize airports from high-resolution optical images. This method involves the analysis of the saliency distribution and the use of fuzzy rule-based classification. First, a number of images with and without airports are segmented into multiple scales to obtain a saliency distribution map that best highlights the saliency distinction between airports and other objects. Then, on the basis of the segmentation result and the structural information of airports, we analyze the segmentation result to extract and represent the semantic information of each image via the bag-of-visual-words (BOVW) model. The image correlation degree is combined with the BOVW model and fractal dimension calculation to make a more complete description of the airports and to carry out preliminary classification. Finally, the support vector machine (SVM) is adopted for detailed classification to classify the remaining imagery. The experiment shows that the proposed method achieves a precision of 89.47% and a recall of 90.67% and performs better than other state of the art methods on precision and recall.
Highlights
Recent advances in the quality and availability of very high resolution (VHR) imagery have opened new prospects in the field of the automatic detection of geospatial objects for multiple purposes [1,2,3]
These methods, while using VHR imagery data, only focus on the extraction of features such as line features, texture features or point features to only detect part of the airport, such as runways, and fail to take other items of the airport into meaningful account, so they are unable to utilize the semantic information of the complete airport to achieve better performance
Zhao et al [15] proposed a saliency-constraint method for airport detection on low-resolution aerial imagery. This method uses a saliency-based constraint to detect the possible regions of interest (ROI) and adapts a refined version of the popular semantic model of the BOVW model to decide whether the ROI is an airport
Summary
Recent advances in the quality and availability of very high resolution (VHR) imagery have opened new prospects in the field of the automatic detection of geospatial objects for multiple purposes [1,2,3]. The proposed method combines the semantics and VHR imagery and adapts image-based analysis of the structural information of airports, which will be further discussed, as well as the use of features to extract semantic information to recognize airports from VHR remote sensing imagery. This method is suitable for VHR imagery, and it does not require heavy dependence on external data or information. It is based on the learning of features of the VHR image to extract semantics for a better representation and recognition of airports.
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