Combining Satellite Images with Feature Indices for Improved Change Detection

This paper presents a novel approach to detect changes in satellite images taken from the same location at different timestamps. Different change detection methods are applied to multispectral satellite images taken with the Worldview-2 (WV-2) satellite, as well as to several of their feature indices such as normalized difference vegetation index (NDVI), normalized difference soil index (NDSI), non-homogeneous feature index (NHFD) and red-blue ratio (R/B). Besides, an additional image is used to remove temporary changes like vehicles, persons etc. The combination of changes is computed with a set of pixel-wise operations, and morphological filters are applied to improve the final change map. The combination of the satellite images with their feature indices proved to produce better results than computing the changes independently. This paper summarizes the methodology and presents the results obtained.

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

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

[3]  Jiang Li,et al.  Seagrass Detection in Coastal Water Through Deep Capsule Networks , 2018, PRCV.

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

[5]  Jiang Li,et al.  Deep Models for Engagement Assessment With Scarce Label Information , 2016, IEEE Transactions on Human-Machine Systems.

[6]  F. Lindsay,et al.  Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations , 2000 .

[7]  R. Colombo,et al.  Integration of remote sensing data and GIS for accurate mapping of flooded areas , 2002 .

[8]  Jiang Li,et al.  A Deep Transfer Learning Approach for Improved Post-Traumatic Stress Disorder Diagnosis , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

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

[10]  C. Unsalan.  Detecting Changes in Multispectral Satellite Images using Time Dependent Angle Vegetation Indices , 2007, 2007 3rd International Conference on Recent Advances in Space Technologies.

[11]  I. Hegazy,et al.  Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt , 2015 .

[12]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[13]  A. Viña,et al.  Drought Monitoring with NDVI-Based Standardized Vegetation Index , 2002 .

[14]  Chiman Kwan,et al.  On the use of radiance domain for burn scar detection under varying atmospheric illumination conditions and viewing geometry , 2017, Signal Image Video Process..

[15]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[16]  Francesca Bovolo,et al.  A support vector domain method for change detection in multitemporal images , 2010, Pattern Recognit. Lett..

[17]  Chiman Kwan,et al.  A Novel Utilization of Image Registration Techniques to Process Mastcam Images in Mars Rover With Applications to Image Fusion, Pixel Clustering, and Anomaly Detection , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Yuzhong Shen,et al.  Deep learning for effective detection of excavated soil related to illegal tunnel activities , 2017, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON).

[19]  Chiman Kwan,et al.  A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Chiman Kwan,et al.  A new nonlinear change detection approach based on band ratioing , 2018, Defense + Security.

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

[22]  Jiang Li,et al.  DeepCoast: Quantifying Seagrass Distribution in Coastal Water Through Deep Capsule Networks , 2018, PRCV.

[23]  K. K. Mayo,et al.  Monitoring land-cover change by principal component analysis of multitemporal Landsat data. , 1980 .

[24]  Chiman Kwan,et al.  Enhancing Mastcam Images for Mars Rover Mission , 2017, ISNN.

[25]  Dinggang Shen,et al.  A Robust Deep Model for Improved Classification of AD/MCI Patients , 2015, IEEE Journal of Biomedical and Health Informatics.

[26]  Chiman Kwan,et al.  Bum scar detection using cloudy MODIS images via low-rank and sparsity-based models , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[27]  Chiman Kwan,et al.  Pansharpening of Mastcam images , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[28]  Liangpei Zhang,et al.  A scene change detection framework for multi-temporal very high resolution remote sensing images , 2016, Signal Process..

[29]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[30]  Chunhong Pan,et al.  Change detection based on auto-encoder model for VHR images , 2013, Other Conferences.

[31]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[32]  Jiang Li,et al.  Detection of seagrass scars using sparse coding and morphological filter , 2018, Remote Sensing of Environment.