A novel remote sensing detection method for buildings damaged by earthquake based on multiscale adaptive multiple feature fusion

Abstract The rapid and accurate detection of damaged buildings after an earthquake are critical for emergency response. Given the difference in the textures of damaged parts and those of the original buildings, damaged buildings can be accurately detected through textural heterogeneity. However, quantitatively detecting damaged buildings using such heterogeneity from post-earthquake images is difficult. Therefore, we propose a method of automatically extracting house damage information from post-quake high-resolution optical remote sensing imagery through the multiscale fusion of spectral and textural features, which can be achieved in three steps. Firstly, the textural and spectral features of the images are enhanced at the pixel level. Secondly, the resulting feature images are fused at the feature level and the fused feature images are segmented using superpixels. Lastly, a post-quake house damage index model is constructed. Results show an overall accuracy of 76.75%, 75.35% and 83.25% for three different types of imagery. This finding indicates that the proposed algorithm can be used to extract damage information from multisource remote sensing data and provide useful guidance for post-disaster rescue and assessment based on regional house damage conditions.

[1]  Faith R. Kearns,et al.  Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography , 2008, Comput. Environ. Urban Syst..

[2]  F. Yamazaki,et al.  Identification of Damaged Areas due to the 2006 Central Java, Indonesia Earthquake Using Satellite Optical Images , 2007, 2007 Urban Remote Sensing Joint Event.

[3]  Wei Liu,et al.  Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake , 2016, Remote. Sens..

[4]  Matti Pietikäinen,et al.  Performance evaluation of texture measures with classification based on Kullback discrimination of distributions , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[5]  Junlin Li,et al.  A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS , 2017 .

[6]  Liangpei Zhang,et al.  A New Building Extraction Postprocessing Framework for High-Spatial-Resolution Remote-Sensing Imagery , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Weisheng Wang,et al.  A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm , 2017, Sensors.

[8]  Xinming Tang,et al.  A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use/Land Cover (LULC) Classification in Cloudy Mountainous Area , 2020 .

[9]  Na Zhao,et al.  Mapping of Urban Surface Water Bodies from Sentinel-2 MSI Imagery at 10 m Resolution via NDWI-Based Image Sharpening , 2017, Remote. Sens..

[10]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[11]  Jie Shan,et al.  A comprehensive review of earthquake-induced building damage detection with remote sensing techniques , 2013 .

[12]  George Vosselman,et al.  Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks , 2018, Remote. Sens..

[13]  Mark van der Meijde,et al.  Regolith modeling and its relation to earthquake induced building damage: A remote sensing approach , 2011 .

[14]  Kunlong Yin,et al.  Object-oriented change detection and damage assessment using high-resolution remote sensing images, Tangjiao Landslide, Three Gorges Reservoir, China , 2018, Environmental Earth Sciences.

[15]  Bing Guo,et al.  An Optimal Monitoring Model of Desertification in Naiman Banner Based on Feature Space Utilizing Landsat8 Oli Image , 2020, IEEE Access.

[16]  Richard Lepage,et al.  Fast and Efficient Evaluation of Building Damage From Very High Resolution Optical Satellite Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Dongsheng Wei,et al.  Detecting damaged buildings using a texture feature contribution index from post-earthquake remote sensing images , 2020 .

[18]  Fei Yang,et al.  Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image , 2020 .

[19]  Kosin Chamnongthai,et al.  Fusion of color histogram and LBP-based features for texture image retrieval and classification , 2017, Inf. Sci..

[20]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[21]  Yang Liu,et al.  Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge , 2018, ISPRS Int. J. Geo Inf..

[22]  Natalia Sofina,et al.  Building Change Detection Using High Resolution Remotely Sensed Data and GIS , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  Pierre Soille,et al.  Extracting building stock information from optical satellite imagery for mapping earthquake exposure and its vulnerability , 2013, Natural Hazards.

[24]  F. Samadzadegan,et al.  Simultaneous feature selection and SVM parameter determination in classification of hyperspectral imagery using Ant Colony Optimization , 2012 .

[25]  Zhiwei Liu,et al.  SLIC segmentation method for full-polarised remote-sensing image , 2019 .

[26]  Han Dong,et al.  Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector , 2019, Applied Sciences.

[27]  Christiane Weber,et al.  Towards a rapid automatic detection of building damage using remote sensing for disaster management : The 2010 Haiti earthquake , 2014 .

[28]  X. Tong,et al.  Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake , 2012 .

[29]  Hao Wu,et al.  An object-based image analysis for building seismic vulnerability assessment using high-resolution remote sensing imagery , 2014, Natural Hazards.

[30]  Naoto Yokoya,et al.  Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia , 2019, Remote. Sens..

[31]  Qian Du,et al.  Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors , 2016, Remote. Sens..

[32]  Rui Zhang,et al.  Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy , 2018, Sensors.

[33]  Xian Sun,et al.  A generic discriminative part-based model for geospatial object detection in optical remote sensing images , 2015 .