Automatic Reference Image Selection for Color Balancing in Remote Sensing Imagery Mosaic

Selection of a reference image is an important step in color balancing. However, the past research and currently available methods do not focus on it, leading to the lack of an effective way to select the reference image for color balancing in remote sensing imagery mosaic. This letter proposes a novel automatic reference image selection method that aims to select the reference images by assessing multifactors according to the land surface types of the target images. The proposed method addresses the limitations caused by the use of a single assessment factor as well as the selection of a single image as the reference in traditional methods. In addition, the proposed method has a wider range of applications than those requiring no reference image. The visual experimental results indicate that the proposed method can select the suitable reference images, which benefits the color balancing result, and outperforms the other comparative methods. Moreover, the absolute mean value of skewness metric of the proposed method is 0.0831, which is lower than the values of the other comparison methods. It indicates that the result of the proposed method had the best performance in the color information. The quantitative analyses with the metric of absolute difference of mean value indicate that the proposed method has a good ability in maintaining the spectral information, and the spectral changing rates had been reduced at least 10.66% by the proposed method when compared with the other methods.

[1]  Bo Du,et al.  Automatic Radiometric Normalization for Multitemporal Remote Sensing Imagery With Iterative Slow Feature Analysis , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Muhammad Twaha Ibrahim,et al.  Automatic selection of color reference image for panoramic stitching , 2015, Multimedia Systems.

[3]  Rémi Cresson,et al.  Natural Color Satellite Image Mosaicking Using Quadratic Programming in Decorrelated Color Space , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Xiuguang Zhou.  Multiple Auto-Adapting Color Balancing for Large Number of Images , 2015 .

[5]  E. Helmer,et al.  Cloud-Free Satellite Image Mosaics with Regression Trees and Histogram Matching. , 2005 .

[6]  Xiang Zhu,et al.  Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content , 2010, IEEE Transactions on Image Processing.

[7]  Xiaolin Wu,et al.  A Linear Programming Approach for Optimal Contrast-Tone Mapping , 2011, IEEE Transactions on Image Processing.

[8]  E. Adelson,et al.  Image statistics and the perception of surface qualities , 2007, Nature.

[9]  David A. Clausi,et al.  Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Manchun Li,et al.  Parallel relative radiometric normalisation for remote sensing image mosaics , 2014, Comput. Geosci..

[11]  Mi Wang,et al.  A Network-Based Radiometric Equalization Approach for Digital Aerial Orthoimages , 2010, IEEE Geoscience and Remote Sensing Letters.

[12]  Yingen Xiong,et al.  Fast panorama stitching for high-quality panoramic images on mobile phones , 2010, IEEE Transactions on Consumer Electronics.

[13]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[14]  Ivor W. Tsang,et al.  Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..

[15]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.