Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6): 193-198.doi: 10.11896/jsjkx.210500058

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Remote Sensing Change Detection Based on Feature Fusion and Attention Network

LAN Ling-xiang, CHI Ming-min   

  1. Shanghai Key Laboratory of Data Science,Fudan University,Shanghai 200438,China
  • Received:2021-05-10 Revised:2021-06-04 Online:2022-06-15 Published:2022-06-08
  • About author:LAN Ling-xiang,born in 1996,postgraduate.His main research interests include deep learning,remote sensing and change detection.
    CHI Ming-min,born in 1976,Ph.D,associate professor.Her main research interests include data science,big data,and machine learning with applications to remote sensing,intelligent manufacturing,oil and gas exploration,compu-ter vision and astronomy.
  • Supported by:
    National Key R & D Program of China(2017YFA0402600) and Science and Technology Research Project of Sinopec(PE19003-3).

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Abstract: Change detection is one of the essential tasks in remote sensing,which is usually regarded as a pixel-level classification problem.In recent years,deep neural networks have also been widely used in the change detection task due to their powerful hierarchical representation of bi-temporal images.A feature fusion and attention network (FFAN) is proposed based on neural encoder-fusion-decoder framework.It integrates features generated by encoder with the bi-temporal difference feature enhanced by attention mechanism,to better capture the bi-temporal change information.In particular,bi-temporal features enhanced by attention mechanism can significantly enhance the propagation of change information in the intermediate layers of deep networks,which adaptively recalibrates the change activation in FFAN by explicitly modeling the interdependence of bi-temporal inputs.Experiments conducted on open-source dataset demonstrate that,compared with existing methods,FFAN obtains better performance.

Key words: Attention mechanism, Change detection, Deep learning, Feature fusion, Remote sensing

CLC Number: 

  • TP391
[1] LIANG B,WENG Q.Assessing urban environmental qualitychange of Indianapolis,United States,by the remote sensing and GIS integration[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2010,4(1):43-55.
[2] LEI T,ZHANG Y,LV Z,et al.Landslide inventory mappingfrom bitemporal images using deep convolutional neural networks[J].IEEE Geoscience and Remote Sensing Letters,2019,16(6):982-986.
[3] LIU S,CHI M,ZOU Y,et al.Oil spill detection via multitemporal optical remote sensing images:A change detection perspective[J].IEEE Geoscience and Remote Sensing Letters,2017,14(3):324-328.
[4] QUARMBY N A,CUSHNIE J L.Monitoring urban land cover changes at the urban fringe from SPOT HRV imagery in south-east England[J].International Journal of Remote Sensing,1989,10(6):953-963.
[5] HOWARTH P J,WICKWARE G M.Procedures for change detection using Landsat digital data[J].International Journal of Remote Sensing,1981,2(3):277-291.
[6] CARVALHO J O A,GUIMARÃES R F,GILLESPIE A R,et al.A new approach to change vector analysis using distance and similarity measures[J].Remote Sensing,2011,3(11):2473-2493.
[7] JIN S,SADER S A.Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances[J].Remote sensing of Environment,2005,94(3):364-372.
[8] NIELSEN A A,CONRADSEN K,SIMPSON J J.Multivariatealteration detection (MAD) and MAF postprocessing in multispectral,bitemporal image data:New approaches to change detection studies[J].Remote Sensing of Environment,1998,64(1):1-19.
[9] DENG J S,WANG K,DENG Y H,et al.PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data[J].International Journal of Remote Sensing,2008,29(16):4823-4838.
[10] MANTERO P,MOSER G,SERPICO S B.Partially supervised classification of remote sensing images through SVM-based probability density estimation[J].IEEE Transactions on Geo-science and Remote Sensing,2005,43(3):559-570.
[11] IM J,JENSEN J R.A change detection model based on neighborhood correlation image analysis and decision tree classification[J].Remote Sensing of Environment,2005,99(3):326-340.
[12] ZHAO J,GONG M,LIU J,et al.Deep learning to classify diffe-rence image for image change detection[C]//International Joint Conference on Neural Networks.IEEE,2014:411-417.
[13] ZHANG H,GONG M,ZHANG P,et al.Feature-level change detection using deep representation and feature change analysis for multispectral imagery[J].IEEE Geoscience and Remote Sensing Letters,2016,13(11):1666-1670.
[14] WANG Q,YUAN Z,DU Q,et al.GETNET:A general end-to-end 2-D CNN framework for hyperspectral image change detection[J].IEEE Transactions on Geoscience and Remote Sensing,2018,57(1):3-13.
[15] EL AMIN A M,LIU Q,WANG Y.Convolutional neural network features based change detection in satellite images[C]//First International Workshop on Pattern Recognition.International Society for Optics and Photonics,2016.
[16] DAUDT R C,LE SAUX B,BOULCH A.Fully convolutionalsiamese networks for change detection[C]//International Confe-rence on Image Processing.IEEE,2018:4063-4067.
[17] LAN L,WU D,CHI M.Multi-temporal Change Detection based on Deep Semantic Segmentation Networks[C]//International Workshop on the Analysis of Multitemporal Remote Sensing Images.IEEE,2019:1-4.
[18] CHEN H,SHI Z.A spatial-temporal attention-based methodand a new dataset for remote sensing image change detection[J].Remote Sensing,2020,12(10):1662.
[19] ZHANG C,YUE P,TAPETE D,et al.A deeply supervisedimage fusion network for change detection in high resolution bi-temporal remote sensing images[J].ISPRS Journal of Photogrammetry and Remote Sensing,2020,6(166):183-200.
[20] PENG D,ZHANG Y,GUAN H.End-to-end change detectionfor high resolution satellite images using improved UNet++[J].Remote Sensing,2019,11(11):1382.
[21] CHEN L,ZHANG D,LI P,et al.Change Detection of RemoteSensing Images Based on Attention Mechanism[J].Computational Intelligence and Neuroscience,2020,(1):1-11.
[22] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Computer Vision and Pattern Recognition.IEEE,2015:3431-3440.
[23] HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Computer Vision and Pattern Recognition.IEEE,2016:770-778.
[24] WANG P,CHEN P,YUAN Y,et al.Understanding convolution for semantic segmentation[C]//Winter Conference on Applications of Computer Vision.IEEE,2018:1451-1460.
[25] HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Computer Vision and Pattern Recognition.IEEE,2018:7132-7141.
[26] LEBEDEV M A,VIZILTER Y V,VYGOLOV O V,et al.Change Detection in Remote Sensing Images Using Conditional Adversarial Networks[J].International Archives of the Photogrammetry,Remote Sensing & Spatial Information Sciences,2018,42(2):565-571.
[27] HE J,ZHANG C Q,LI X Z,et al.Survey of Research on Multimodal Fusion Technology for Deep Learning[J].Computer Engineering,2020,46(5):1-11.
[28] CHEN J,YUAN Z,PENG J,et al.DASNet:Dual attentive fully convolutional siamese networks for change detection of high re-solution satellite images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020,14:1194-1206.
[29] ZHOU B,KHOSLA A,LAPEDRIZA A,et al.Learning deep features for discriminative localization[C]//Computer Vision and Pattern Recognition.IEEE,2016:2921-2929.
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