Automated Cloud Removal and Filling in Optical Remote Sensing Images

This paper proposes a novel method for automatically removing and filling cloud regions in optical remote sensing images. Based on frequency-tuned saliency, an improved saliency algorithm is proposed to identify cloud regions. A cloud map in a binary image is used to remove the identified cloud regions. Digital Elevation Model (DEM) that represents authentic terrain features of the remote sensing image is applied to fill the removed cloud regions. The DEM is transformed as hypsometric tint, the color of which is changed to be the same as that of the remote sensing image in Lab color space. For well blending the edge between the DEM and the remote sensing image, a mosaic blending algorithm is presented by building a diamond-shaped structure with gradual change near the edge. Therefore, a well combined remote sensing image that can represent the authentic feature of the earth surface can be obtained.

[1]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[2]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[3]  Chao-Hung Lin,et al.  Cloud Removal From Multitemporal Satellite Images Using Information Cloning , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Tony F. Chan,et al.  Non-texture inpainting by curvature-driven diffusions (CDD) , 2001 .

[5]  Junwei Han,et al.  Real-time Rendering of Large-scale Terrain in the Flight Simulator , 2007, 2007 IEEE International Conference on Integration Technology.

[6]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[7]  Tony F. Chan,et al.  Mathematical Models for Local Nontexture Inpaintings , 2002, SIAM J. Appl. Math..

[8]  Daniel Cohen-Or,et al.  Fragment-based image completion , 2003, ACM Trans. Graph..

[9]  Guillermo Sapiro,et al.  Simultaneous structure and texture image inpainting , 2003, IEEE Trans. Image Process..

[10]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[11]  Ali N. Akansu,et al.  A class of fast Gaussian binomial filters for speech and image processing , 1991, IEEE Trans. Signal Process..

[12]  Manuel Menezes de Oliveira Neto,et al.  Fast Digital Image Inpainting , 2001, VIIP.

[13]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[14]  Fen Chen,et al.  Clouds and cloud shadows removal from high-resolution remote sensing images , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[15]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[16]  S. Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, CVPR 2009.