A High-Fidelity Haze Removal Method Based on HOT for Visible Remote Sensing Images

Spatially varying haze is a common feature of most satellite images currently used for land cover classification and mapping and can significantly affect image quality. In this paper, we present a high-fidelity haze removal method based on Haze Optimized Transformation (HOT), comprising of three steps: semi-automatic HOT transform, HOT perfection and percentile based dark object subtraction (DOS). Since digital numbers (DNs) of band red and blue are highly correlated in clear sky, the R-squared criterion is utilized to search the relative clearest regions of the whole scene automatically. After HOT transform, spurious HOT responses are first masked out and filled by means of four-direction scan and dynamic interpolation, and then homomorphic filter is performed to compensate for loss of HOT of masked-out regions with large areas. To avoid patches and halo artifacts, a procedure called percentile DOS is implemented to eliminate the influence of haze. Scenes including various land cover types are selected to validate the proposed method, and a comparison analysis with HOT and Background Suppressed Haze Thickness Index (BSHTI) is performed. Three quality assessment indicators are selected to evaluate the haze removed effect on image quality from different perspective and band profiles are utilized to analyze the spectral consistency. Experiment results verify the effectiveness of the proposed method for haze removal and the superiority of it in preserving the natural color of object itself, enhancing local contrast, and maintaining structural information of original image.

[1]  Daniel Schläpfer,et al.  Combined Haze and Cirrus Removal for Multispectral Imagery , 2016, IEEE Geoscience and Remote Sensing Letters.

[2]  Yong Du,et al.  Haze detection and removal in high resolution satellite image with wavelet analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[3]  Huifang Li,et al.  An effective thin cloud removal procedure for visible remote sensing images , 2014 .

[4]  B. Guindon,et al.  ROBUST HAZE REDUCTION: AN INTEGRAL PROCESSING COMPONENT IN SATELLITE-BASED LAND COVER MAPPING , 2002 .

[5]  Lena Halounová,et al.  Haze removal for high‐resolution satellite data: a case study , 2007 .

[6]  Xiaoyu Li,et al.  A haze removal module for mutlispectral satellite imagery , 2009, 2009 Joint Urban Remote Sensing Event.

[7]  Hongliang Fang,et al.  Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods , 2001, IEEE Trans. Geosci. Remote. Sens..

[8]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[9]  P. Chavez An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[10]  Bert Guindon,et al.  Quantitative assessment of a haze suppression methodology for satellite imagery: effect on land cover classification performance , 2003, IEEE Trans. Geosci. Remote. Sens..

[11]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[12]  O. R. Mitchell,et al.  Filtering to remove cloud cover in satellite imagery , 1977, IEEE Transactions on Geoscience Electronics.

[13]  Wei Tang,et al.  Single Remote Sensing Image Dehazing , 2014, IEEE Geoscience and Remote Sensing Letters.

[14]  C. Woodcock,et al.  Monitoring land-use change in the Pearl River Delta using Landsat TM , 2002 .

[15]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..

[16]  Yves Rosseel,et al.  On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data , 2013, PloS one.

[17]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[18]  Jianbo Hu,et al.  Haze detection, perfection and removal for high spatial resolution satellite imagery , 2011 .

[19]  Craig S. T. Daughtry,et al.  Atmospheric correction of Landsat ETM+ land surface imagery. II. Validation and applications , 2002, IEEE Trans. Geosci. Remote. Sens..

[20]  R. Richter A spatially adaptive fast atmospheric correction algorithm , 1996 .

[21]  Bobby R. Hunt,et al.  A new approach to removing cloud cover from satellite imagery , 1984, Comput. Vis. Graph. Image Process..

[22]  Eric P. Crist,et al.  A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[23]  J. Cihlar,et al.  An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images , 2002 .

[24]  Shree K. Nayar,et al.  Contrast Restoration of Weather Degraded Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[26]  Peter Reinartz,et al.  Haze Detection and Removal in Remotely Sensed Multispectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.