Slope-Restricted Multi-Scale Feature Matching for Geostationary Satellite Remote Sensing Images

For geostationary meteorological satellite (GSMS) remote sensing image registration, high computational cost and matching error are the two main challenging problems. To address these issues, this paper proposes a novel algorithm named slope-restricted multi-scale feature matching. In multi-scale feature matching, images are subsampled to different scales. From a small scale to a large scale, the offsets between the matched pairs are used to narrow the searching area of feature matching for the next larger scale. Thus, the feature matching is accomplished from coarse to fine, which will make the matching process more accurate and reduce errors. To enhance the matching performance, the outliers in the matched pairs are rectified by using slope-restricted rectification, which is based on local geometric similarity. Compared with other algorithms, the experimental results show that our proposed method is more accurate and efficient.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  Guohua Gu,et al.  Polynomial fitting-based shape matching algorithm for multi-sensors remote sensing images , 2016 .

[3]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[4]  Jacqueline Le Moigne,et al.  An automated parallel image registration technique based on the correlation of wavelet features , 2013, IEEE Trans. Geosci. Remote. Sens..

[5]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[6]  Walter H. F. Smith,et al.  A global, self‐consistent, hierarchical, high‐resolution shoreline database , 1996 .

[7]  David J. Harding,et al.  Automatic Image Registration of Multimodal Remotely Sensed Data With Global Shearlet Features , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Chunhong Pan,et al.  Registration of Optical and SAR Satellite Images by Exploring the Spatial Relationship of the Improved SIFT , 2013, IEEE Geoscience and Remote Sensing Letters.

[9]  Qi Tian,et al.  Neighborhood geometry based feature matching for geostationary satellite remote sensing image , 2017, Neurocomputing.

[10]  Jacqueline Le Moigne,et al.  Shearlet features for registration of remotely sensed multitemporal images , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[11]  Maoguo Gong,et al.  Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering , 2012, IEEE Transactions on Image Processing.

[12]  Qi Tian,et al.  Packing and Padding: Coupled Multi-index for Accurate Image Retrieval , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Arturo Espinosa-Romero,et al.  A robust Graph Transformation Matching for non-rigid registration , 2009, Image Vis. Comput..

[14]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[15]  Amin Sedaghat,et al.  Distinctive Order Based Self-Similarity descriptor for multi-sensor remote sensing image matching , 2015 .

[16]  马文萍.  A Novel Point-Matching Algorithm Based on Fast Sample Consensus for Image Registration , 2014 .

[17]  Lei Huang,et al.  SAR and optical images registration using shape context , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[18]  Xiaojing Huang,et al.  Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision , 2015, Remote. Sens..

[19]  Dimitri Lague,et al.  3D Terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology , 2011, ArXiv.

[20]  Pengfei Xu,et al.  A novel algorithm of remote sensing image fusion based on Shearlets and PCNN , 2013, Neurocomputing.

[21]  Jubai An,et al.  A Simple and Robust Feature Point Matching Algorithm Based on Restricted Spatial Order Constraints for Aerial Image Registration , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Kai Zhang,et al.  A Robust Point-Matching Algorithm for Remote Sensing Image Registration , 2014, IEEE Geoscience and Remote Sensing Letters.

[23]  Jacqueline Le Moigne,et al.  On the use of wavelets for image registration , 2011, Image Registration for Remote Sensing.

[24]  Liangpei Zhang,et al.  Sparse-based reconstruction of missing information in remote sensing images from spectral/temporal complementary information , 2015 .

[25]  Yan Wu,et al.  SAR Image Change Detection Based on Iterative Label-Information Composite Kernel Supervised by Anisotropic Texture , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[26]  C. Lawrence Zitnick,et al.  Fast Edge Detection Using Structured Forests , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Wei Jie,et al.  A review of parallel computing for large-scale remote sensing image mosaicking , 2015, Cluster Computing.

[28]  Amin Sedaghat,et al.  Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Hassan Ghassemian,et al.  Remote Sensing Image Fusion Using Ripplet Transform and Compressed Sensing , 2015, IEEE Geoscience and Remote Sensing Letters.

[30]  Qi Tian,et al.  Coupled Binary Embedding for Large-Scale Image Retrieval , 2014, IEEE Transactions on Image Processing.

[31]  Jie Jiang,et al.  A Robust Point-Matching Algorithm Based on Integrated Spatial Structure Constraint for Remote Sensing Image Registration , 2016, IEEE Geoscience and Remote Sensing Letters.

[32]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[33]  Feifei Zhang,et al.  Robust Image Registration Using Structure Features , 2014, IEEE Geoscience and Remote Sensing Letters.

[34]  Ying Liang,et al.  The improved algorithm of remote sensing image registration based on SIFT and CONTOURLET transform , 2013, 2013 IEEE 4th International Conference on Software Engineering and Service Science.