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A two-level fusion for building irregularity detection in post-disaster VHR oblique images

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Abstract

A post-disaster scene is more complicated than a vertical pre-disaster one, since it includes debris-covered areas and the imagery angle may be oblique. Detecting buildings and their irregularities in post-disaster oblique images is a valuable resource in many remote sensing applications, such as building damage assessment, which is a critical and challenging task in the disaster management cycle. We proposed a two-level fusion method, which is fast enough to support save and rescue missions. Firstly, an edge-based knowledge-based approach is designed to extract the vertical building map from temporal pre-disaster data. This approach utilizes Gray-Level Co-Occurrence Matrix (GLCM) features and shadow information. The temporal analysis helps the procedure to find stable objects by reducing the effect of noise in fine-resolution data. The mentioned two-level fusion includes data and decision levels. Spectral and georeferenced bands of pre- and post-disaster samples are fused in the data level. The decision-level refers to fusing the results of two classifiers, Spectral-only and Geo-Spectral classifiers. The locality of the method is preserved by the georeferenced feature. The proposed method is implemented on the Google Earth Engine (GEE) platform, and the evaluation is based on Hurricane Nate (2017) and Hurricane Harvey (2017) oblique images. The proposed method shows a significant reduction in the false-positive error, and provides a high performance in detecting irregularity in facade and rooftop areas.

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References

  • Adams S, Friedland C, Levitan M Unmanned aerial vehicle data acquisition for damage assessment in hurricane events. In: Proceedings of the 8th International Workshop on Remote Sensing for Disaster Management, Tokyo, Japan, 2010

  • Akinlar C, Topal C (2011) EDLines: A real-time line segment detector with a false detection control. Pattern Recognition Letters 32:1633–1642

    Google Scholar 

  • Asokan A, Anitha J (2019) Change detection techniques for remote sensing applications: a survey. Earth Science Informatics 12:143–160

    Google Scholar 

  • Bapu JJ et al (2019) Adaptive convolutional neural network using N-gram for spatial object recognition. Earth Sci Inf 12:525–540

    Google Scholar 

  • Belgiu M, Drăguţ L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114:24–31

    Google Scholar 

  • Bittner K, Adam F, Cui S, Körner M, Reinartz P (2018) Building Footprint Extraction From VHR Remote Sensing Images Combined with Normalized DSMs Using Fused Fully Convolutional Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11:2615–2629

    Google Scholar 

  • Boonpook W, Tan Y, Ye Y, Torteeka P, Torsri K, Dong S (2018) A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring Sensors 18:3921

  • Boulila W (2019) A top-down approach for semantic segmentation of big remote sensing images Earth Science Informatics:1–12

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Google Scholar 

  • Breiman L (2017) Classification and regression trees. Routledge,

  • Briem GJ, Benediktsson JA, Sveinsson JR (2002) Multiple classifiers applied to multisource remote sensing data. IEEE transactions on geoscience and remote sensing 40:2291–2299

    Google Scholar 

  • Chen R, Li X, Li J (2018) Object-based features for house detection from rgb high-resolution images Remote Sensing 10:451

  • Cheng G, Han J (2016) A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 117:11–28

    Google Scholar 

  • Cohen JP, Ding W, Kuhlman C, Chen A, Di L (2016) Rapid building detection using machine learning. Applied Intelligence 45:443–457

    Google Scholar 

  • Doustfatemeh I, Baleghi Y (2016) Comprehensive urban area extraction from multispectral medium spatial resolution remote-sensing imagery based on a novel structural feature. International Journal of Remote Sensing 37:4225–4242

    Google Scholar 

  • Du P, Xia J, Zhang W, Tan K, Liu Y, Liu S (2012) Multiple classifier system for remote sensing image classification: A review Sensors 12:4764–4792

  • Feng Q, Liu J, Gong J (2015) Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—a case of Yuyao. China Water 7:1437–1455

    Google Scholar 

  • Gao X, Wang M, Yang Y, Li G (2018) Building Extraction From RGB VHR Images Using Shifted Shadow Algorithm IEEE Access 6:22034–22045

  • Ghadjati M, Moussaoui A, Boukharouba A (2019) A novel iterative PCA–based pansharpening method. Remote sensing letters 10:264–273

    Google Scholar 

  • Ghandour AJ, Jezzini AA (2018) Autonomous Building Detection Using Edge Properties and Image Color Invariants Buildings 8:65

  • Ghanea M, Moallem P, Momeni M (2016) Building extraction from high-resolution satellite images in urban areas: recent methods and strategies against significant challenges. International Journal of Remote Sensing 37:5234–5248

    Google Scholar 

  • Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27

    Google Scholar 

  • Hall-Beyer M (2004) GLCM Texture: A Tutorial v. 2.7. 1, on-line document

  • Han J, Mao K, Xu T, Guo J, Zuo Z, Gao C (2018) A soil moisture estimation framework based on the cart algorithm and its application in china. Journal of hydrology 563:65–75

    Google Scholar 

  • Haralick RM, Shanmugam K (1973) Textural features for image classification IEEE Transactions on systems, man, and cybernetics:610–621

  • Hedjam R, Kalacska M, Mignotte M, Nafchi HZ, Cheriet M (2016) Iterative classifiers combination model for change detection in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 54:6997–7008

    Google Scholar 

  • Huang X, Zhang L (2011) A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery. Photogrammetric Engineering & Remote Sensing 77:721–732

    Google Scholar 

  • Huang X, Yuan W, Li J, Zhang L (2017) A new building extraction postprocessing framework for high-spatial-resolution remote-sensing imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10:654–668

    Google Scholar 

  • Hussain M, Chen D, Cheng A, Wei H, Stanley D (2013) Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing 80:91–106

    Google Scholar 

  • Ji S, Wei S, Lu M (2018) A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery International Journal of Remote Sensing:1–15

  • Jiang A, Xiao S, Wei L, Zhu Y Research on Multiple Classifiers Combination Method for Remote Sensing Images. In: International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, 2017. Springer, pp 353–361

  • Jolliffe I (2003) Principal component analysis Technometrics 45:276

    Google Scholar 

  • Kakooei M, Baleghi Y (2017) Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment international. journal of remote sensing 38:2511–2534

    Google Scholar 

  • Kakooei M, Baleghi Y (2020) Shadow detection in very high resolution RGB images using a special thresholding on a new spectral–spatial index. Journal of Applied Remote Sensing 14:016503

    Google Scholar 

  • Kakooei M, Baleghi Y Leaf-Less-Tree feature for semantic labeling applications on Google Earth Engine. In: Telecommunications (IST), 2018 9th international symposium on, 2018. IEEE,

  • Kakooei M, Baleghi Y Spectral Unmixing of Time Series Data to Provide Initial Object Seeds for Change Detection on Google Earth Engine. In: 2019 27th Iranian conference on electrical engineering (ICEE), 2019. IEEE, pp 1402–1407

  • Li G, Wan Y (2015) A new combination classification of pixel-and object-based methods. international journal of remote sensing 36:5842–5868

    Google Scholar 

  • Li E, Femiani J, Xu S, Zhang X, Wonka P (2015) Robust rooftop extraction from visible band images using higher order. CRF IEEE Transactions on Geoscience and Remote Sensing 53:4483–4495

    Google Scholar 

  • Li E, Xu S, Meng W, Zhang X (2017a) Building extraction from remotely sensed images by integrating saliency cue. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10:906–919

    Google Scholar 

  • Li S, Tang H, Huang X, Mao T, Niu X (2017b) Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters Remote Sensing 9:1177

  • Li X, Yao X, Fang Y (2018) Building-A-Nets: Robust Building Extraction From High-Resolution Remote Sensing Images With Adversarial Networks IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing:1–8

  • Li Y, Hu W, Dong H, Zhang X (2019) Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector Applied Sciences 9:1128

  • Lin Y, Zhang B, Xu J, Li J, Zhao C, Yu D (n.d.). Hierarchical Building Extraction from High-resolution Remote Sensing Imagery Based on Multi-feature and Multi-scale Method. In: Proceedings of the 3rd International Conference on Multimedia and Image Processing, 2018. ACM, pp 17–23

  • Liu J, Liu Y local regularity-driven city-scale facade detection from aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014. pp 3778–3785

  • Liu Y et al. (2018) Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing:1–13

  • Lu T, Ming D, Lin X, Hong Z, Bai X, Fang J (2018) Detecting Building Edges from High Spatial Resolution Remote Sensing Imagery Using Richer Convolution Features Network Remote Sensing 10:1496

  • Lussem U, Bolten A, Gnyp M, Jasper J, Bareth G (2018) EVALUATION OF RGB-BASED VEGETATION INDICES FROM UAV IMAGERY TO ESTIMATE FORAGE YIELD IN GRASSLAND ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences:1215–1219

  • Manno-Kovács A, Ok AO (2015) Building detection from monocular VHR images by integrated urban area knowledge. IEEE Geoscience and Remote Sensing Letters 12:2140–2144

    Google Scholar 

  • Marcu A, Costea D, Slusanschi E, Leordeanu M (2018) A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization arXiv preprint arXiv:180401322

  • Martel E et al. (2018) Implementation of the principal component analysis onto high-performance computer facilities for hyperspectral dimensionality reduction: results and comparisons Remote Sensing 10:864

  • Mather P, Tso B (2016) Classification methods for remotely sensed data. CRC press,

  • Mishra VN, Prasad R, Rai PK, Vishwakarma AK, Arora A (2019) Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data. Earth Science Informatics 12:71–86

    Google Scholar 

  • Mitchell HB (2010) Image fusion: theories, techniques and applications. Springer Science & Business Media,

  • Myint SW, Yuan M, Cerveny RS, Giri CP (2008) Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data Sensors 8:1128–1156

  • National Agriculture Imagery Program (NAIP) (n.d.). Farm Service Agency. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/. Accessed 2019 2019

  • Ngo T-T, Collet C, Mazet V Automatic rectangular building detection from VHR aerial imagery using shadow and image segmentation. In: Image Processing (ICIP), 2015 IEEE International Conference on, 2015. IEEE, pp 1483–1487

  • Ngo T-T, Mazet V, Collet C, De Fraipont P (2017) Shape-Based Building Detection in Visible Band Images Using Shadow Information IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10:920–932

  • NOAA (n.d.). Emergency Reponse. https://www.ngs.noaa.gov/RSD/erp.shtml. 2019

  • Ok AO (2013) Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS journal of photogrammetry and remote sensing 86:21–40

    Google Scholar 

  • Ok AO, Senaras C, Yuksel B (2013) Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery. IEEE Transactions on Geoscience and Remote Sensing 51:1701–1717

    Google Scholar 

  • Otsu N (1979) A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 9:62–66

    Google Scholar 

  • Pal M (2005) Random forest classifier for remote sensing classification. international journal of remote sensing 26:217–222

    Google Scholar 

  • Pan S, Guan H, Yu Y, Li J, Peng D (2019) A comparative land-cover classification feature study of learning algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12:1314–1326

  • Pirasteh S et al. (2019) Developing an algorithm for buildings extraction and determining changes from airborne LiDAR, and comparing with R-CNN method from drone images remote sensing 11:1272

  • Pohl C, Van Genderen J (2016) Remote sensing image fusion: a practical guide. Crc Press,

  • Puttinaovarat S, Horkaew P (2019) Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation Earth Science Informatics:1–18

  • Qin X, Jagersand M, Yang X, Wang J Building facade recognition from aerial images using Delaunay Triangulation induced feature perceptual grouping. In: Pattern Recognition (ICPR), 2016 23rd International Conference on, 2016. IEEE, pp 3368–3373

  • Radhika S, Tamura Y, Matsui M (2012) Use of post-storm images for automated tornado-borne debris path identification using texture-wavelet analysis. Journal of Wind Engineering and Industrial Aerodynamics 107:202–213

    Google Scholar 

  • Radhika S, Tamura Y, Matsui M (2015) Cyclone damage detection on building structures from pre-and post-satellite images using wavelet based pattern recognition. Journal of Wind Engineering and Industrial Aerodynamics 136:23–33

    Google Scholar 

  • Rastiveis H, Eslamizade F, Hosseini-Zirdoo E (2015) Building damage assessment after earthquake using post-event LiDAR data the international archives of photogrammetry. Remote Sensing and Spatial Information Sciences 40:595

    Google Scholar 

  • Richards JA, Jia X (2006) Remote sensing digital image analysis remote sensing digital image analysis, an introduction, by John a Richards and Xiuping Jia 2006 XXV, 439 p 197 illus 4th ed 3-540-25128-6 Berlin: springer, 2006:197

  • Salgado C, Zortea M, Scharcanski J (2018) Classify-normalize-classify Earth Science Informatics 11:77–97

    Google Scholar 

  • Serpico SB, Dellepiane S, Boni G, Moser G, Angiati E, Rudari R (2012) Information extraction from remote sensing images for flood monitoring and damage evaluation Proceedings of the IEEE 100:2946–2970

  • Shen H, Lin Y, Tian Q, Xu K, Jiao J (2018) A comparison of multiple classifier combinations using different voting-weights for remote sensing image classification. International journal of remote sensing 39:3705–3722

    Google Scholar 

  • Shi W, Mao Z (2016) Building extraction from panchromatic high-resolution remotely sensed imagery based on potential histogram and neighborhood Total variation earth science informatics 9:497-509

  • Shi W, Mao Z, Liu J (2018) Building area extraction from the high spatial resolution remote sensing imagery Earth Science Informatics:1–11

  • Shlens J (2014) A tutorial on principal component analysis arXiv preprint arXiv:14041100

  • Sun L, Tang Y, Zhang L (2017) Rural building detection in high-resolution imagery based on a two-stage CNN model IEEE Geoscience and Remote Sensing Letters 14:1998–2002

    Google Scholar 

  • Tan K, Jin X, Plaza A, Wang X, Xiao L, Du P (2016) Automatic change detection in high-resolution remote sensing images by using a multiple classifier system and spectral–spatial features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9:3439–3451

    Google Scholar 

  • Thomas J, Kareem A, Bowyer K Recent advances towards a robust, automated hurricane damage assessment from high-resolution images. In: Advances in Hurricane Engineering: Learning from Our Past, 2013. ASCE, pp 806–815

  • Tian J, Nielsen AA, Reinartz P (2015) Building damage assessment after the earthquake in Haiti using two post-event satellite stereo imagery and DSMs. International Journal of Image and Data Fusion 6:155–169

    Google Scholar 

  • Tong X et al (2012) Building-damage detection using pre-and post-seismic high-resolution satellite stereo imagery: a case study of the may 2008 Wenchuan earthquake. ISPRS J Photogramm Remote Sens 68:13–27

    Google Scholar 

  • Turker M, Koc-San D (2015) Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. International Journal of Applied Earth Observation and Geoinformation 34:58–69

    Google Scholar 

  • Uddin MP, Mamun MA, Hossain MA (2019) Effective feature extraction through segmentation-based folded-PCA for hyperspectral image classification. International Journal of Remote Sensing 40:7190–7220

    Google Scholar 

  • Upadhyay V, Kumar A (2018) Hyperspectral remote sensing of forests: technological advancements, opportunities and challenges. Earth Sci Inf 11:487–524

    Google Scholar 

  • Wang J, Yang X, Qin X, Ye X, Qin Q (2015) An efficient approach for automatic rectangular building extraction from very high resolution optical satellite imagery. IEEE Geoscience and Remote Sensing Letters 12:487–491

    Google Scholar 

  • Wei D, Yang W (2020) Detecting damaged buildings using a texture feature contribution index from post-earthquake remote sensing images. Remote Sensing Letters 11:127–136

    Google Scholar 

  • Yang X, Qin X, Wang J, Wang J, Ye X, Qin Q (2015) Building façade recognition using oblique aerial images Remote Sensing 7:10562–10588

  • You Y, Wang S, Ma Y, Chen G, Wang B, Shen M, Liu W (2018) Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index Remote Sensing 10:1287

    Google Scholar 

  • Yuan J (2016) Automatic building extraction in aerial scenes using convolutional networks arXiv preprint arXiv:160206564

  • Zhang Q, Huang X, Zhang G (2016) A morphological building detection framework for high-resolution optical imagery over urban areas. IEEE Geoscience and Remote Sensing Letters 13:1388–1392

    Google Scholar 

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Acknowledgments

The authors acknowledge the funding support of Babol Noshirvani University of Technology through Grant program No. BNUT/370123/98.

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  1. Electrical and Computer Engineering Department, Babol Noshirvani University of Technology, Babol, Iran

    Mohammad Kakooei

  2. Electrical and Computer Engineering Department, Babol Noshirvani University of Technology, Babol, Iran

    Yasser Baleghi

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  1. Mohammad Kakooei
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  2. Yasser Baleghi
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Correspondence to Yasser Baleghi.

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Communicated by: H. Babaie

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Kakooei, M., Baleghi, Y. A two-level fusion for building irregularity detection in post-disaster VHR oblique images. Earth Sci Inform 13, 459–477 (2020). https://doi.org/10.1007/s12145-020-00449-6

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