CHANGE DETECTION VIA SELECTIVE GUIDED CONTRASTING FILTERS

Abstract. Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

[1]  R. Kauth,et al.  The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat , 1976 .

[2]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[3]  Philip J. Howarth,et al.  Procedures for change detection using Landsat digital data , 1981 .

[4]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[5]  Robert A. Schowengerdt,et al.  Techniques for image processing and classification in remote sensing , 1983 .

[6]  John A. Richards,et al.  Thematic mapping from multitemporal image data using the principal components transformation , 1984 .

[7]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[8]  R. C. Maggio,et al.  An analysis of anthropogenic deforestation using logistic regression and GIS , 1990 .

[9]  Vladimir Naumovich Vapni.  The Nature of Statistical Learning Theory , 1995 .

[10]  C. Woodcock,et al.  An assessment of several linear change detection techniques for mapping forest mortality using multitemporal landsat TM data , 1996 .

[11]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[12]  R. Lunetta,et al.  Remote Sensing Change Detection: Environmental Monitoring Methods and Applications , 1999 .

[13]  Pierre Soille,et al.  Morphological Image Analysis , 1999 .

[14]  S. Khorram,et al.  Remotely Sensed Change Detection Based on Artificial Neural Networks , 1999 .

[15]  J. Mas.  Monitoring land-cover changes: A comparison of change detection techniques , 1999 .

[16]  W. D. Ross,et al.  Change Detection using Adaptive Fuzzy neural networks : Environmental damage assessment after the Gulf War , 1999 .

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  Curtis E. Woodcock,et al.  Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors , 2001 .

[19]  D. Roberts,et al.  A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery , 2002 .

[20]  C. Özkan,et al.  Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities , 2004 .

[21]  D. Lu,et al.  Change detection techniques , 2004 .

[22]  Zaher Dawy,et al.  An approximation to the distribution of finite sample size mutual information estimates , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[23]  John R. Jensen,et al.  A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .

[24]  T. Blaschke.  TOWARDS A FRAMEWORK FOR CHANGE DETECTION BASED ON IMAGE OBJECTS , 2005 .

[25]  Amir Averbuch,et al.  Objects based change detection in a pair of gray-level images , 2005, Pattern Recognit..

[26]  Bryan C. Pijanowski,et al.  Calibrating a neural network‐based urban change model for two metropolitan areas of the Upper Midwest of the United States , 2005, Int. J. Geogr. Inf. Sci..

[27]  Suming Jin,et al.  Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances , 2005 .

[28]  D. Al-Khudhairy,et al.  Structural Damage Assessments from Ikonos Data Using Change Detection, Object-Oriented Segmentation, and Classification Techniques , 2005 .

[29]  R. Lunetta,et al.  Land-cover characterization and change detection using multitemporal MODIS NDVI data , 2005, International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005..

[30]  C. Tucker,et al.  A comparative study of NOAA–AVHRR derived drought indices using change vector analysis , 2006 .

[31]  Wei Ji,et al.  Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics , 2006, Comput. Environ. Urban Syst..

[32]  Hassiba Nemmour,et al.  Multiple support vector machines for land cover change detection: An application for mapping urban extensions , 2006 .

[33]  StudyAlessandro Campa,et al.  Maximization of Mutual Informationin a Linear Noisy Network : a Detailed , 2007 .

[34]  Laurent Durieux,et al.  A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data , 2008 .

[35]  D. Stow,et al.  Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery , 2008 .

[36]  Jinsong Deng,et al.  PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data , 2008 .

[37]  Chengquan Huang,et al.  Use of a dark object concept and support vector machines to automate forest cover change analysis , 2008 .

[38]  Gregory Duveiller,et al.  Deforestation in Central Africa: Estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts , 2008 .

[39]  Laurence Hubert-Moy,et al.  Object-Oriented Approach and Texture Analysis for Change Detection in Very High Resolution Images , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[40]  Xia Li,et al.  Cellular automata for simulating land use changes based on support vector machines , 2008, Comput. Geosci..

[41]  Francesca Bovolo,et al.  A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[42]  P. Marpu,et al.  Change detection using object features , 2008 .

[43]  Jay Gao.  Digital Analysis of Remotely Sensed Imagery , 2009 .

[44]  M. Kelly,et al.  Individual Object Change Detection for Monitoring the Impact of a Forest Pathogen on a Hardwood Forest , 2009 .

[45]  N. Chang,et al.  Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed. , 2009, Journal of environmental management.

[46]  Philippe De Maeyer,et al.  Object-oriented change detection for the city of Harare, Zimbabwe , 2009, Expert Syst. Appl..

[47]  Dong-Chen He,et al.  Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge , 2010 .

[48]  Hichem Sahbi,et al.  Constrained optical flow for aerial image change detection , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[49]  Ashish Ghosh,et al.  Fuzzy clustering algorithms for unsupervised change detection in remote sensing images , 2011, Inf. Sci..

[50]  Daniel Tomowski,et al.  Colour and texture based change detection for urban disaster analysis , 2011, 2011 Joint Urban Remote Sensing Event.

[51]  S. Zheltov,et al.  GEOMETRICAL CORRELATION AND MATCHING OF 2D IMAGE SHAPES , 2012 .

[52]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[53]  Dongmei Chen,et al.  Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .

[54]  L. M. Mestetskiy,et al.  Morphological Image Analysis for Computer Vision Applications , 2015 .

[55]  Yu. V. Vizilter,et al.  CHANGE DETECTION VIA MORPHOLOGICAL COMPARATIVE FILTERS , 2016 .