Building detection plays an important role in urban planning, change detection, surface coverage and so on. However, in high resolution remote sensing images, buildings vary in shape, color, and size, which makes building detection a difficult problem. Therefore, this paper proposes a method based on multi-scale and multi-feature to automatically extract buildings in high resolution images: Firstly, down sampling images are used to construct Gauss pyramid model, while fixed size windows in different layers of pyramid image represent different ground areas. Then multi features are calculated which describe building characteristics by sliding windows, and multi features are fused to evaluate the saliency of building in different scales. Then the saliency of superpixels is calculated, and Otsu algorithm is used to automatically determine the threshold, and furthermore, some constraints such as the aspect ratio were combined to extract buildings accurately and automatically. Experiments were made by 0.5 m and 0.2 m high resolution remote sensing images in comparison with the markov random field model based on color and texture modeling algorithm for qualitative and quantitative comparison. The results show that the method suggested in this paper can obtain more satisfactory precision and has higher effect on building detection from high-resolution remote sensing images.
吴柳青, 胡翔云. 基于多尺度多特征的高空间分辨率遥感影像建筑物自动化检测[J]. 国土资源遥感, 2019, 31(1): 71-78.
Liuqing WU, Xiangyun HU. Automatic building detection of high-resolution remote sensing images based on multi-scale and multi-feature. Remote Sensing for Land & Resources, 2019, 31(1): 71-78.
Meng Y . Key Technologies Research of Illegal Building Detection Based on 3S Technology[D]. Beijing:Institute of Remote Sensing Applications of Chinese Academy of Sciences, 2009.
Lyu F H, Shu N, Gong Y , et al. Regular building extraction from high resolution image based on multilevel-features[J]. Geomatics and Information Science of Wuhan University, 2017,42(5):656-660.
Tan Q L . Urban building extraction from VHR multi-spectral images using object-based classification[J]. Acta Geodaetica et Cartographica Sinica, 2010,39(6):618-623.
Xu H G, Song Y . Change detection method taking into account shadow information for high resolution remote sensing image[J]. Remote Sensing for Land and Resources, 2013,25(4):16-21.doi: 10.6046/gtzyyg.2013.04.03.
Pang C H, Li G Y, Zhao J , et al. Building figure extraction in satellite images based on line detection algorithm[J]. Computer Applications, 2008,28(s1):190-192.
Zhao C, Zhang B M, Chen X W , et al. A method of extracting building based on LiDAR point clouds[J]. Bulletin of Surveying and Mapping, 2017,( 2):35-39.
Wang X, Li P J, Jiang S S , et al. Building extraction using airborne LiDAR data and very high resolution imagery over a complex urban area[J]. Remote Sensing for Land and Resources, 2016,28(2):106-111.doi: 10.6046/gtzyyg.2016.02.17.
[9]
Vakalopoulou M, Karantzalos K, Komodakis N, et al. Building detection in very high resolution multispectral data with deep learning features [C]//Geoscience and Remote Sensing Symposium,IEEE, 2015: 1873-1876.
Chen W K . Remote sensing image detection of rural buildings based on deep learning algorithm[J]. Surveying and Mapping, 2016,39(5):227-230.
[11]
Wang H Y, Pan D L, Xia D S . Fast algorithm for two-dimensional Otsu adaptive threshold algorithm[J]. Journal of Image and Graphics, 2005,33(9):968-971.
doi: 10.1360/aas-007-0968
[12]
Hu X Y, Shen J J, Shan J , et al. Local edge distributions for detection of salient structure textures and objects[J]. IEEE Geoscience and Remote Sensing Letters, 2013,10(3):466-470.
doi: 10.1109/LGRS.2012.2210188
[13]
Alexe B, Deselaers T, Ferrari V . Measuring the objectness of image windows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,34(11):2189-2202.
doi: 10.1109/TPAMI.2012.28
pmid: 22248633
[14]
Cheng Y Z . Mean Shift,mode seeking,and clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995,17(8):790-799.
doi: 10.1109/34.400568
[15]
Gu W, Lyu Z H, Hao M . Change Detection Method for Remote Sensing Images Based on An Improved Markov Random Field[M]. The Netherlands:Kluwer Academic Publishers, 2017.