Automatic airport recognition from a remote sensing image is popular in the remote-sensing community. However

most studies extracted objects from cropped images because performing the same task on a high-resolution image is time consuming. Most studies extracted the image of a straight line

which is determined based on airport runways

and then determined the target; however

the straight line is not only a high-resolution image extraction of airport runways

but may also be of highways

railways

external walls of large-scale plants

marginal lands

mountains

formations

and other structures. Methods of distinguishing extracted straight runways are rarely studied. Furthermore

these methods focused on a single and large airport instead of a small one

and few of these methods studied multiple targets. China has recently launched a high-resolution mapping satellite that has global reach data acquisition and real-time data transmission ability. Beyond surveying and mapping

the mapping satellite can also be used for land cover classification

land object recognition

and resource exploration. In this paper

mapping satellite imagery is used (with 6 000×6 000 pixels) as a source to detect airport images that have rich details and complex land covers. Traditional spatial filter and edge-detection methods tend to fail in airport detection because of noise and false edges. To solve this problem

an edge-tracing model combined with speeded up robust features (SURF) detection is proposed. This paper presents a new method that combines edge tracing and SURF to extract airport images. Edge tracing is based on edge extraction. First

filtering is applied to suppress noise. Then

gradient magnitude and gradient direction along norms are calculated. Greatest local variations of gradients found by improved non-maximum suppression (NMS) method are deleted to obtain a single-pixel edge image. A contour line is extracted from this edge image. Finally

airports are detected through line detection and SURF. Airports in four mapping satellite images are successfully recognized using the presented method. Improved NMS and edge-contour tracing are helpful for edge extraction. Most of the object edges are successfully detected and are considerably clear with single-edge width. After the line detection for ground objects

a SURF detector is applied to recognize airports in the image. Two airports with different sizes

one large and one small

are successfully detected and extracted from two mapping satellite images

which demonstrate the effectiveness of the proposed method. The proposed method is considerably effective in airport detection. The airport detection method proposed in this paper is suitable not only for remote-sensing data of mapping satellites but also for application on day-painting satellites

such as other remote-sensing satellite data. The improved NMS method can extract more accurate and thinner edges while suppressing false edges. This method is extremely helpful for airport detection. The edge-tracing model can extract contours in different directions accurately

thereby improving line detection accuracy. Many ground targets

such as roads

railways

croplands

walls of factories and mines

mountains

strata

and others

can be recognized from line features. Thus

SURF is used to distinguish airports from these objects. Our experiments show that many key points extracted by SURF lie on the intersection of different lines inside the airports. Based on this observation

the airport can be identified by scanning images to find lines and key points.