2009 Urban Remote Sensing Joint Event

978-1-4244-3461-9/09/$25.00 ©2009 IEEE

A haze removal module for mutlispectral satellite

imagery

Jianbo Hua,b, Wei Chena, Xiaoyu Lia ,Xingyuan Hea,*, aInstitute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China

bGraduate School of the Chinese Academy of Sciences, Beijing 100039, China

E-mail addresses: WhoGamble@qq.com (Jianbo Hu), Hexy@iae.ac.cn (Xingyuan He).

Abstract-Ever-present spatial varying haze contamination in sat-

ellite scenes limits applications, especially urban remote sensing

using visible and near infrared bands of low temporal resolution

multispectral satellite imageries. We introduce a haze removal

module coded in IDL language (used with ENVI software) which

is a three-step framework comprised of haze detection, haze per-

fection and haze removal. The framework is based on several

previous researches, loose and open, so that users can pick any

method contained in each step or develop and use their own

methods. Some TM and QuickBird images are present for visual

assessment. Shortcoming of this module is too much human in-

tervention on parameter determination and further search

should aim at automation and more new methods in each step.

I. INTRODUCTION

Ever-present spatial varying haze contamination in satellite scenes limits applications, especially urban remote sensing using visible and near infrared bands of low temporal resolu-tion multispectral satellite imageries. Haze partially obscures the ground, so it is theoretically possible to be removed, though existing atmospheric correction techniques developed under homogeneous atmospheric condition are helpless.

Previous researches on image-based haze removal tech-nique can be grouped into two methods, spatial filtering and spectrum transformation. Spatial filtering such as FFT and wavelet analysis [1], gets rid of the lower frequency layer where haze is distributed, but also loses a component of land cover information. Spectrum transformation including cluster matching [2, 3], Tasselled Cap (TC) transform [4, 5] and haze optimized transformation (HOT) [6, 7] detects haze before haze removal. Cluster matching developed is based on an as-sumption that each land cover cluster (unsupervised classified using infrared bands) has the same visible reflectance in both clear and hazy regions, which is sometimes not true in a com-plex environment, especially urban environment. What’s more, cluster mathing is developed for TM/ETM and not suitable for VHR. HOT is the advanced form of TC using only blue and red bands for haze detection, and dark object subtraction (DOS) is implemented on each band of each slice after density slicing HOT image. HOT is proved to be an operational haze removal technique for TM/ETM and VHR using visible bands only. However, limitation of HOT is the precondition of high correlation between blue band and red band, which is some-times not true. Furthermore, an overcorrection and undercor-

rection problem of some land cover types needs further re-search.

Recently, we have done researches in different aspects to improve HOT and also found another haze detection method as substitution when correlation between blue and red bands are not tight enough. Manuscripts are both under review by remote sensing journals (hereafter referred to by ‘manuscript1’ [8] and ‘manuscript2’ [9]) and methods are in the same three-step framework that is comprised of haze detection, haze perfection and haze removal. In this paper, module of this framework will be introduced briefly and some TM and QuickBird images of natural and urban environment are present for visual assess-ment..

II. HAZE REMOVAL MODULE

Module is coded in IDL language (used with ENVI soft-ware, http://www.sciencenet.cn/upload/blog/file/2008/11/20081127182552448710.rar, User Guide document is in Chinese at pre-sent). After placing the ‘haze_tool.sav’ file in the ‘save_add’ directory of ENVI installation, and restarting ENVI, you will find one more ‘Haze tool’ button added at the end of the ‘Basic tools’ menu (Fig. 1). Since our objective is to develop haze removal algorithms, so it is not a commercial module and we did not spent much time on the interface.

Figure 1. Haze removal module embedded in the software ENVI at the end of the ‘Basic tools’ menu.

The three-step framework of this module is illustrated in Fig. 2. Three steps are organized and should be used in se-quence (haze perfection is optinal), containing more than one methods in each step. The framework is loose and open, so that users can pick any method contained in each step developed by us or develop their own methods.

2009 Urban Remote Sensing Joint Event

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Figure 2. Three-step framework of the haze removal module. Haze detection: HOT = haze optimized transformation in [6], BSHTI = background suppressed haze thickness index in [9]. Haze perfection: AL = adjust bias of

each land cover type (for TM) in [8], MMI = mask manually and interpolation in [10], TSI = threshold segmentation and interpolation (not recommended), FS = fill sink operation in [8, 9], FP = flatten peak operation in [8, 9]. Haze

removal: DOS = dark object subtraction in [6], VCP = virtual cloud point in manuscript [8, 9], HM = histogram matching in [4, 5]

A. Haze detection

This step is to determine relative haze thickness (not aero-sol optical depth) through spectrum transformation. Right now, we have HOT [6] and BSHTI (Background Suppressed Haze Thickness Index, [9]) and selection is dependent (Fig. 3). Ac-cording to our experience, if correlation coefficient of blue and red bands in the clear region of an image is large enough which is the precondition of HOT, it is appropriate to choose either one, or BSHTI is the only one by now.

Figure 3. Comparison of HOT and BSHTI as haze thickness index. The left

is hazy TM image (R/G/B = 4/3/1), middle is BSHTI, and right is HOT. HOT is inappropriate in the upper case, and both BSHTI and HOT are appropriate

in the lower case.

B. Haze perfection

This optional step is to correct spurious value (haze thick-ness index overestimated or underestimated) resulted from haze detection caused by some land cover types through using spatial information. Right now, we have five methods includ-ing AL, MMI, TSI, FS, FP (full names of these acronyms are in Fig. 1, detailed in corresponding articles and manuscripts). Different from haze detection and haze removal, methods in this step are not exclusive and users can select them as needed (Table 1).

TABLE I. INSTRUCTIONS OF METHODS IN HAZE PERFECTION STEP.

Instruction

AL Several infrared bands needed (such as TM), not suitable for four-band high resolution images. It is based on the assumption that land cover types classified using infrared

bands are nearly the same with that classified using three visible bands, which is not strict for some special land cover types

MMI Feasible when number of spurious value patches are small, or will be time consuming.

TSI Only suitable under simple condition when overestimated

values are larger than the largest correct value of haze and underestimated values are not contaminated by haze.

FS recommended to correct underestimated values using

mathematical morphological operations FP recommended to correct overestimated values using

mathematical morphological operations

This step is the most subjective step in this module and need much human intervention to decide which method to choose and to set some parameters. We recommend our manu-scripts to users for details. Here we only illustrated their func-tion and efficiency. Since MMI and TSI are comprehensive, we illustrate AL, FS, FP using one hazy (suburban) and one clear (urban) subsets of a typical TM image (Fig. 4), which are de-tailed step by step in [8].

2009 Urban Remote Sensing Joint Event

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Hazy subset Clear subset

HOT

AL

FS

FP

Figure 4. Visual assessment on efficiency of methods in haze perfection step.

C. Haze removal

This step use the original hazy multispectral image and haze image (HOT or BSHTI after haze perfection) as input. Right now, we have three methods including DOS, HM (histo-gram matching), VCP (virtual cloud point, detailed in [8, 9]). All of them are implemented on each band separately, after density slicing the haze image. VCP has been proved to be better than DOS in our previous study [8]. According to our experience, HM performs well when haze is homogenous since pixel number in each slice is large enough to ensure similarity of land cover composition between hazy and clear regions , while VCP performs well when haze thickness is spatial vary-ing which is a common situation.

In this paper, we present some TM and QuickBird images for visual assessment on efficiency of this module by be-fore-and-after comparison (Fig. 5). Images are selected with

different land cover type composition and haze situation which needs different method combination to remove haze.

2009 Urban Remote Sensing Joint Event

978-1-4244-3461-9/09/$25.00 ©2009 IEEE

Figure 5. Visual assessment by before-and-after comparison (R/G/B =

4/3/1). A is a TM image of natural environment contaminated by homogenous haze (seems like mist) and processed by HOT+(null)+HM. B is a TM image of natural envrionment contaminated by smoke from fire and processed by

BSHTI+(null)+VCP. C is a TM image of natural envrionment contaminated by spatial varying haze and processed by HOT+(AL)+VCP. D is a TM image

of suburban area contaminated by spatial varying haze and processed by

HOT+(AL+FS+FP)+VCP. E is a QuickBIrd image of urban envrionment contaminated by spatial varying thin haze and processed by

HOT+(FS+FP)+VCP. F is a QuickBird image of urban environment

contaminated by spatial varying thick haze and processed by HOT+(FS+FP)+VCP.

III. CONCLUSION

We have briefly introduced a haze removal module devel-oped for multispectral satellite imagery. The three-step frame-work of this module is synthesized based on previous several research papers and our recently submitted manuscripts. The key point is combination of spectral (haze detection using spectrum transformation) and spatial information (haze perfec-tion using different spatial operations), which makes this mod-ule firstly practical in a complex environment, especially urban environment.

This module is loose and open, can be regard as a haze toolbox containing a lot of methods. Method selection de-pends on experience by now, since this module is an embryo needs further research aiming at automation and further de-velopment on more robust methods in each step.

REFERENCES

[1] Y. Du, B. Guindon, and J. Cihlar, “Haze detection and removal in

high resolution satellite image with wavelet analysis,” IEEE

Transactions on Geoscience and Remote Sensing, vol. 40, no. 1, pp. 210-217, Jan, 2002.

[2] S. L. Liang, H. L. Fang, and M. Z. Chen, “Atmospheric correction of landsat ETM+ land surface imagery - Part I: Methods,” IEEE

Transactions on Geoscience and Remote Sensing, vol. 39, no. 11,

pp. 2490-2498, Nov, 2001. [3] S. L. Liang, H. L. Fang, J. T. Morisette et al., “Atmospheric cor-

rection of landsat ETM plus land surface imagery - Part II: Valida-

tion and applications,” IEEE Transactions on Geoscience and Re-

mote Sensing, vol. 40, no. 12, pp. 2736-2746, Dec, 2002. [4] R. Richter, “A spatially adaptive fast atmospheric correction algo-

rithm,” International Journal of Remote Sensing, vol. 17, no. 6, pp. 1201-1214, Apr, 1996.

[5] R. Richter, “Atmospheric correction of satellite data with haze

removal including a haze/clear transition region,” Computers &

Geosciences, vol. 22, no. 6, pp. 675-681, Jul, 1996. [6] Y. Zhang, B. Guindon, and J. Cihlar, “An image transform to

characterize and compensate for spatial variations in thin cloud contamination of Landsat images,” Remote Sensing of Environ-

ment, vol. 82, no. 2-3, pp. 173-187, Oct, 2002.

[7] Y. Zhang, and B. Guindon, “Quantitative assessment of a haze suppression methodology for satellite imagery: Effect on land cover classification performance,” IEEE Transactions on Geo-

science and Remote Sensing, vol. 41, no. 5, pp. 1082-1089, May, 2003.

[8] X. Y. He, J. B. Hu, W. Chen et al., “Haze removal based on ad-

vanced haze optimized transformation (AHOT) for multispectral imagery,” http://www.sciencenet.cn/upload/blog/file/2008/11/200811301323

1644946.pdf. [9] J. B. Hu, W. Chen, X. Y. Li et al., “Haze detection, perfection and

removal for high spatial resolution satellite imagery,”

http://www.sciencenet.cn/upload/blog/file/2008/11/200811301316

2129917.pdf. [10] G. Dal Moro, and L. Halounova, “Haze removal for

high-resolution satellite data: A case study,” International Journal

of Remote Sensing, vol. 28, no. 10, pp. 2187-2205, May 20, 2007.