Mr. Junjun Li Profile

Junjun Li

at Chang'an Univ

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SPIE Journal Paper | 27 April 2020

Superpixel-based imaging for residential area detection of high spatial resolution remote sensing imagery

Junjun Li, Jiannong Cao, Yingying Zhu, Muleta Ebissa Feyissa, Beibei Chen

JARS, Vol. 14, Issue 02, 026507, (April 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.026507

KEYWORDS: Spatial resolution, Remote sensing, Image segmentation, Sensors, Buildings, Corner detection, Earth observing sensors, High resolution satellite images, Image filtering, Feature extraction

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The precise and efficient location of residential areas using high spatial resolution remote sensing imagery is a popular research area in the field of Earth observation. Most of the existing approaches are supervised or semisupervised and use data training. Among the unsupervised approaches, corner density-based mapping using kernel density estimate has been widely employed to predict the presence of built-up areas. However, it is computationally time-consuming and the statistical threshold segmentation makes it difficult to obtain a stable and accurate output. To overcome this deficiency, a new two-stage object-oriented residential area extraction scheme was designed. First, a set of corners was extracted using the Gabor filter bank with structural tensor analysis to indicate candidate buildings. Then, instead of pixel units, our method takes superpixel-based image partitions as the primary calculation elements, and an object-oriented weighted sparse spatial voting technique was proposed to accelerate the generation of a residential area presence index. It was demonstrated that the superpixel-based voting strategy was not only efficient in accelerating the calculation process, but it also reduced the false negative rate in the final detection result. Second, a graph-cut method was employed to address the residential area segmentation by integrating a density map as a prior cue that preserves the boundary accuracy better than traditional statistical threshold methods. The effectiveness of the proposed method was evaluated using a series of experiments on the sets of high-resolution Google Earth, IKONOS, and GaoFen-2 (GF2) satellite imagery. The results showed that the proposed approach outperforms the existing algorithms in terms of computational speed and accuracy.

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