Open AccessCRITICAL ASSESSMENT OF OBJECT SEGMENTATION IN AERIAL IMAGE USING GEO-HAUSDORFF DISTANCE
Author(s) -
Haili Sun,
Y. Ding,
Yixiang Huang,
G. Wang
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b4-187-2016
Subject(s) - aerial image , segmentation , artificial intelligence , computer science , computer vision , hausdorff distance , ground truth , image segmentation , context (archaeology) , pixel , scale space segmentation , image resolution , segmentation based object categorization , pattern recognition (psychology) , remote sensing , geography , image (mathematics) , archaeology
Aerial Image records the large-range earth objects with the ever-improving spatial and radiometric resolution. It becomes a powerful tool for earth observation, land-coverage survey, geographical census, etc., and helps delineating the boundary of different kinds of objects on the earth both manually and automatically. In light of the geo-spatial correspondence between the pixel locations of aerial image and the spatial coordinates of ground objects, there is an increasing need of super-pixel segmentation and high-accuracy positioning of objects in aerial image. Besides the commercial software package of eCognition and ENVI, many algorithms have also been developed in the literature to segment objects of aerial images. But how to evaluate the segmentation results remains a challenge, especially in the context of the geo-spatial correspondence. The Geo-Hausdorff Distance (GHD) is proposed to measure the geo-spatial distance between the results of various object segmentation that can be done with the manual ground truth or with the automatic algorithms.Based on the early-breaking and random-sampling design, the GHD calculates the geographical Hausdorff distance with nearly-linear complexity. Segmentation results of several state-of-the-art algorithms, including those of the commercial packages, are evaluated with a diverse set of aerial images. They have different signal-to-noise ratio around the object boundaries and are hard to trace correctly even for human operators. The GHD value is analyzed to comprehensively measure the suitability of different object segmentation methods for aerial images of different spatial resolution. By critically assessing the strengths and limitations of the existing algorithms, the paper provides valuable insight and guideline for extensive research in automating object detection and classification of aerial image in the nation-wide geographic census. It is also promising for the optimal design of operational specification of remote sensing interpretation under the constraints of limited resource.