Abstract
Abstract. The automatic extraction of man-made objects from remote sensing imagery is useful in many applications. This paper proposes an algorithm for extracting man-made objects automatically by integrating a graph model with the manifold ranking algorithm. Initially, we estimate a priori value of the man-made objects with the use of symmetric and contrast features. The graph model is established to represent the spatial relationships among pre-segmented superpixels, which are used as the graph nodes. Multiple characteristics, namely colour, texture and main direction, are used to compute the weights of the adjacent nodes. Manifold ranking effectively explores the relationships among all the nodes in the feature space as well as initial query assignment; thus, it is applied to generate a ranking map, which indicates the scores of the man-made objects. The man-made objects are then segmented on the basis of the ranking map. Two typical segmentation algorithms are compared with the proposed algorithm. Experimental results show that the proposed algorithm can extract man-made objects with high recognition rate and low omission rate.
Highlights
The automatic extraction of man-made objects, such as buildings, roads and bridges, from remote sensing images is one of the fundamental but challenging tasks in remote sensing and computer vision
Remote sensing images have been widely used in man-made object extraction
State-of-the-art algorithms for man-made object extraction can be divided into two categories: feature-based and model-based algorithms
Summary
The automatic extraction of man-made objects, such as buildings, roads and bridges, from remote sensing images is one of the fundamental but challenging tasks in remote sensing and computer vision. We have observed that man-made objects often present symmetric appearances as well as high contrast with adjacent land cover types We utilize these cues in this work to extract a priori value of man-made objects from remote sensing images, and optimize it on the basis of multiple features to obtain the final result. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3, 2018 ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, 7–10 May, Beijing, China machine learning to obtain a symmetry prior These two prior images are integrated to generate a man-made object priori map, from which initial seeds are generated for the manifold ranking algorithm.
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