MODIS IMAGES RESTORATION FOR VNIR BANDS ON FIRE SMOKE AFFECTED AREA

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1 MODIS IMAGES RESTORATION FOR VNIR BANDS ON FIRE SMOKE AFFECTED AREA Li-Yu Chang and Chi-Farn Chen Center for Space and Remote Sensing Research, National Central University, No. 300, Zhongda Rd., Zhongli District, Taoyuan City 32001, Taiwan KEY WORDS: Fire Smoke, Image Restoration, Inverse Distance Weighted Interpolation, MODIS Images. ABSTRACT: On fire event monitoring, images of MODIS sensor can provide timely information to identify the location and situation of fired areas. Without doubt the fire smoke on MODIS images is the key feature for fire event identification. However, the ground information of visible and near infrared (VNIR) bands is also be blocked by fire smoke. Nevertheless, due to the size of smoke particles and the wavelength of short wave infrared (SWIR), the SWIR radiance can still go through smoke and allow the ground information under the smoke can be revealed on the SWIR bands. Therefore, by using inverse distance weighted (IDW) interpolation approach, the smoke contamination in VNIR bands can be removed and the corresponding ground reflectance of VNIR bands can be interpolated and restored. The experimental result shows that a correlation between the restored image and original image is higher than 0.9 in simulation case. Additionally, in real cases, good visual fidelity in restored images can be observed as well. 1. INTRODUCTION Moderate Resolution Imaging Spectroradiometers (MODIS) are widely used remote sensing tools for environment monitoring. Especially on fire monitoring, the large spatial coverage and daily acquisition capabilities of MODIS sensors make them a common remote sensing image source for capturing ground fire events. In general, the smoke object on MODIS images is an important feature for users to identify fire event. Nevertheless, the smoke is also an unwanted feature that can prevent users from observing the current ground status under smoke. Particularly in visible and near infrared (VNIR) bands of MODIS images, the ground information can almost be blocked by smoke. Fortunately, due to the size of smoke particles and the wavelength of short wave infrared (SWIR), the SWIR radiance can still go through smoke and allow the SWIR bands to provide land information under smoke (Kaufman and Remer, 1994; Kaufman et al., 1997). However, in practical the spectral characteristics of SWIR bands usually are different from that of VNIR bands. This fact may cause inconvenience for general users that only familiar with images of VNIR bands. Furthermore, for smoke contaminated MODIS images, there may also have difficulties in comparing with other data sources that only provide VNIR bands. Therefore, if the information under smoke for VNIR bands can be restored form SWIR bands, the above mentioned drawbacks can be improved. According to literature, VNIR bands information was successfully obtained from SWIR bands of various remote sensing sensors. The spectral responses of visible bands at wavelength 0.49µm and 0.66µm were found correlated with SWIR band by using Landsat TM and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images (Kaufman et al., 1997). This characteristic had been applied to restore the red band information at wavelength 0.677µm by using SWIR band information at wavelength 2.121µm for AVIRIS images with linear regression (De Moura and Galvao, 2003). In addition, by using the reflectance of SWIR band and the estimation of NDVISWIR, the reflectance of visible bands at wavelength 0.466µm and 0.644µm were obtained for MODIS images (Dabin et al., 2008). 1.1 Correlations between VNIR and SWIR bands In order to realize how the reflectance of VNIR bands can be restored by that of SWIR bands, further investigation should be made for the band to band correlations of MODIS image. Figure 1 is a MODIS sample image from product MOD09GA and acquired on 6/21/2012 over west of Colorado state, USA. This image is used to demonstrate the spectral property of MODIS data.

2 SWIR image Figure 1. MODIS sample image for the investigation of band to band correlations. (Images acquired on 6/21/2012) Figure 2 is the corresponding band to band scatter plots and correlations for VNIR and SWIR bands. Note that all the cloud pixels were removed. It can be found that the VNIR bands are more or less linearly correlated to SWIR bands and their correlations in different band combinations are around 0.73 to It implies that the VNIR bands are possible to be modelled by SWIR bands with linear regression approach. This approach was applied to restore the image of VNIR bands under smoke area (Chang and Chen, 2014). However, it also can be found that the linear assumption in some band combinations is comparably weaker than the others. For example, in those band combinations with correlation lower than 0.85, their corresponding pixel distributions seems to be spread away and not concentrated to a straight line. This fact implies that nonlinear relationships exist between those band combinations with lower correlations and linear regressions are probably insufficient to restore the VNIR bands image from corresponding SWIR bands image. Figure 2. Band to band scatter plots and correlations for MODIS sample image.

3 1.2 Property of VNIR bands in SWIR spectral feature space Generally, pixels within a small cluster their relationships between SWIR and VNIR bands should be more the same when compared to that of all image pixels. Therefore, the VNIR reflectance of smoke contaminated pixels can be interpolated by using the VNIR reflectance of neighboring smoke free pixels in SWIR spectral feature space. In this paper an inverse distance weighted (IDW) interpolation is proposed to interpolate the VNIR reflectance of smoke contaminated pixels with neighboring smoke free pixels. 2. METHODOLOGY The proposed method includes three major steps: (1) Detecting masks for cloud and smoke, (2) Restoring VNIR bands pixels by interpolation in spectral feature space of SWIR bands and (3) Using mosaic techniques to smooth discontinuous boundary. Following is the brief description of each step: 2.1 Detecting masks for cloud and smoke The spectral characteristics of cloud and smoke feature in images of VNIR bands normally are similar. Due to cloud pixels can be found in quality assurance (QA) information of MODIS product, the image statistics of cloud pixel can be further obtained and used to find smoke pixels. However, if the source image is cloud free or the smoke cannot correctly derived from image statistics of cloud, image classification or manual operation may be needed to carry out the smoke areas. 2.2 Restoring VNIR bands reflectance by IDW interpolation According to the property of VNIR bands in SWIR spectral feature space, the VNIR bands reflectance of smoke contaminated pixel for can be restored by smoke free pixels by IDW interpolation. The weighting factors needed in IDW interpolation are obtained by using the spectral distance between smoke and smoke free pixels in SWIR spectral feature space. Theoretically the larger weight should be put to the smoke free pixel if the pixel is spectrally closer to the smoke pixel in SWIR feature space during IDW interpolation. 2.3 Using mosaic techniques to smooth discontinuous boundary In order to replace the smoke affected area by restored image and avoid any reflectance discontinuity, a buffer zone is generated and mosaic technique is applied to generate a more reasonable output. 3. EXPERIMENTAL RESULTS In this study, MODIS image restorations for simulated and real smoke contaminated areas are carried out. In simulated case, differences of source image and restored image are obtained and compared. For real case, the restored images are presented for visual inspections. 3.1 Case of simulated smoke contamination Figure 3 is an image by applying the sample image in figure 1 and the pixels inside an elliptical area are assumed to be contaminated by smoke. Figure 4 shows the restored VNIR bands images inside the smoke contaminated area by applying the linear regression and proposed method. Note that the source image are also provide for comparison. According to the visual inspection, in true color composite images it can be found that the images restored by different method are similar and only slightly different from source image in a few bright objects. However, in false color composite images, the similarities between restored images and source image are significantly different. It can be observed that the proposed method performs better than linear regression method in vegetated areas. Performances of images restored by linear regression and proposed method are also quantitatively compared. Table 1 and Table 2 are the mean error and root mean square error (RMSE) of restored reflectance respectively. It can be found that mean error for different methods are all very close to zero. However, for the RMSE, the proposed method performs better in visible bands and almost 2 times better in near infrared (NIR) band. Table 3 shows the correlations of reflectance between restored and source images for different methods. It can be found that the correlation of NIR band can only reach 0.82 while the correlation of others visible bands are around 0.9. However, if proposed method is

4 applied, for all bands the restored image can have similar correlations and all above 0.9. This fact implies that the result of proposed method has better capability to model the existing nonlinear relationships between NIR and SWIR bands and can offer better quality consistency for all VNIR bands. SWIR image Figure 3. Sample images for the case of simulated smoke contamination. The elliptical area are assumed as the area contaminated by smoke and removed from VNIR bands. True color composite of source image True color composite of restored image using linear regression method True color composite of restored image using proposed method False color composite of source image False color composite of restored image using linear regression method False color composite of restored image using proposed method Figure 4. Source and restored images inside the smoke contaminated area.

5 Table 1. Mean errors of restored reflectance. Mean Error Restoration Method Band 0.469μm 0.555μm 0.645μm 0.859μm Linear Regression IDW Interpolation Table 2. RMSE of restored reflectance. RMSE Restoration Method Band 0.469μm 0.555μm 0.645μm 0.859μm Linear Regression IDW Interpolation Table 3. Correlations of reflectance between restored and source images. Correlation Restoration Method Band 0.469μm 0.555μm 0.645μm 0.859μm Linear Regression IDW Interpolation Case of real smoke contamination In this section the images actually contaminated by fire smokes are used to evaluate the performance of proposed method. The source images used in figure 5 and 7 are MODIS MYD09GA product acquired on 6/9/2011 over east of Arizona and MODIS MOD09GA product acquired on 9/17/2014 over central California respectively. In these images forest file events and their fire smokes can be observed clearly. Figure 6 and figure 8 show the restored images by using proposed method. It can be seen that the ground objects in VNIR bands under fire smoke are restored successfully from source images. Figure 5. MODIS images for the performance evaluation of proposed method on the case of real smoke contamination (Source images acquired on 6/9/2011).

6 Figure 6. The restored images for images in figure 5 by using proposed method. Figure 7. MODIS images for the performance evaluation of proposed method on the case of real smoke contamination (Source images acquired on 9/17/2014). Figure 8. The restored images for images in figure 7 by using proposed method. 4. CONCLUSION In this study, based on IDW interpolation, an image restoration method for MODIS visible and NIR bands on fire smoke contamination areas is proposed. According to the verification of results, the restoration of VNIR bands by proposed method can perform better than that by linear regression method. Furthermore, according to the case of the restoration for actual fire smoke contaminated MODIS images, satisfied visual fidelity in restored images can also be observed.

7 REFERENCE Chang, L. Y., Chan, C. F., 2014, MODIS image restoration on fire smoke contaminated areas. Proceeding of International Symposium on Remote Sensing 2014, Busan, Korea, April 2014, CDROM. Dabin, J., Lin, S., Tao, J., Mei, D., A new method to estimate the visible reflectance from short wave infrared wavelength. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(B8), pp De Moura, M. L., Galvão, L. S., Smoke effects on NDVI determination of savannah vegetation types. International Journal of Remote Sensing, 24(21), pp Kaufman, Y. J., Remer, L. A., Detection of forests using mid-ir reflectance: an application for aerosol studies. IEEE Transactions on Geoscience and Remote Sensing, 32(3), pp Kaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B. C., Li, R. R., Flynn, L., The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5), pp