Generation of Cloud-free Imagery Using Landsat-8

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1 Generation of Cloud-free Imagery Using Landsat-8 Byeonghee Kim 1, Youkyung Han 2, Yonghyun Kim 3, Yongil Kim 4 Department of Civil and Environmental Engineering, Seoul National University (SNU), Seoul, , Rep. of Korea, *Corresponding author: ABSTRACT Cloud cover and cloud shadow areas on satellite imagery restrict the practical use of remote sensing data. Thus, cloud screening and filling methods are critical for geospatial users. The recently launched Landsat-8 provides coastal/aerosol and cirrus bands to tackle this problem. In this case, clouds can be accurately detected with Landsat-8 data, and the masked cloud areas could be filled in with image processing methods. This paper presents a novel method for detection and filling of cloud and its shadow areas using the Landsat-8 sensor. First, cloud and its shadow areas are detected from the Landsat-8 bands using Otsu s thresholding method. The detected cloud and cloud-shadow areas are then replaced using coordinates of a reference image and pixel values from an experimental image corresponding to the coordinates of reference image. Experimental results using the Landsat-8 dataset indicated that the proposed method generates a superior quality of cloud free imagery. Key word: Cloud detection, Cloud removal, Landsat-8, Otsu s N thresholding INTRODUCTION Landsat is one of the most popular and oldest satellite data sources for observation of Earth s land surface. A great deal of research using Landsat series data has been conducted for various applications, such as change detection and land-cover classification, because of the medium spatial resolution and spectral variance of these data. However, cloud and its shadows remain serious obstacles when using Landsat data, especially when monitoring land surface. Therefore, a method for replacing the cloud and shadow areas with original land surface is needed to improve image quality and availability when dealing with Landsat data (Xiaolin Zhu, 2012). Many studies have focused on cloud detection, the use of thresholds, and replacement of the cloud and its shadow areas. The study by MA Ying-zhao (2010) on cloud detection used a decision tree based on Landsat-5 thresholds. Min LI (2002) used pixel-ranking, which categorizes the images using thresholds as cloud, shadow, vegetation, and buildings. The results of these studies are good; however, a few disadvantages are noted. For example, the thresholds are chosen by manual inspection, which is time consuming, and the methods are inadequate for applying to any cloud cover images. To tackle this problem, Yi-Shiang (2011) used an automatic thresholds only for thick cloud detection, but did not deal with the shadow areas. Cloud cover areas have been filled using a regression tree and histogram matching (Helmer and Ruefenacht, 2005). Xiaolin Zhu (2012) suggested a modified neighborhood similar pixel interpolator approach, which is generally used to fill gaps in Landsat ETM+ scan line corrector-off images. Suming (2005) devised a concept of a spectral similarity group (SSG) filling method. Recently, Landsat-8 was successfully launched and it has provided greatly enhanced spectral information with the addition of two new spectral bands: a deep blue visible channel (band 1) and a new infrared channel (band 9). These new bands allow the problem caused by cloud and shadow to be resolved more easily compared with previous Landsat-5 and 7 images. The infrared channel (band 9), called the cirrus band, is useful for cloud detection because its wavelength is from to 1.390μm. This wavelength of band 9 includes a strong water vapor absorption wavelength area, and water vapor is generally concentrated in the lower atmosphere (Hutchison, 1996), which means that incident solar energy in this channel diminishes prior to reaching the land surface. When this incident solar energy is reflected into space, a similar phenomenon will happen. However, this process does not happen in cloud

2 areas because water and ice in the cloud block incident solar energy and reflect it directly into space. For this reason, DN (Digital Number) is higher in cloud areas than at other surfaces, and cloud areas can be easily detected. This paper proposes a process for generating a cloud-free image based on Landsat-8 images. Two sets of Landsat-8 images were acquired, one of which included cloud areas. Automatic thresholds, using Otsu s thresholding, were applied to the experimental image in order to create a cloud and cloud shadow masking area. This masking area was ultimately filled in with coordinates from a reference image and pixel values from an experimental image corresponding to the coordinates of the reference image. MATERIALS AND METHODS Materials The proposed method was applied to Landsat-8 satellite images, with 30m spatial resolution in a multispectral image, acquired in April, May, and, June, Two study sites were chosen, including two images per site: one experiment scene, which includes cloud and its shadow area and one reference scene, which is a clear image taken at a different time (Table 1, Figure 1). Table 1. Landsat data sets for generation of cloud free images Path / Row Experiment image Reference image 38 / May June / April May 2013 Figure 1. Experimental (left) and Reference (right) images (R, G, B), (a) P38/R35, (b) P28/R45 Landsat-8 has two new bands in addition to those provided by Landsat-5 and 7. These new bands are helpful for detecting clouds, so fewer bands are needed to detect clouds and cloud shadows. The generation of a cloud-free image consists of two major parts (figure 2). The first part produces a cloud and cloud-shadow mask, using Otsu s thresholding method. The second part fills in the cloud and cloud-

3 shadow area, based on an SSG filling method (Suming, 2013). Figure 2. Flow chart Cloud masks The process of generating a cloud mask is divided into two parts: thick cloud masking and thin cloud masking. Both processes used spectral information of Landsat bands 1, 9, and 10 and Otsu s thresholding method was applied. Otsu s thresholding is an automatic method for finding an optimum global threshold value in a histogram (Otsu, 1975). Thick cloud was detected by selecting thresholds of bands 9 and 10. Thick cloud usually has high values in band 9, because water and ice in the cloud area usually block penetration of light. Therefore, incident solar energy is strongly reflected in the cloud area at the wavelength of band 9. On the other hand, the temperature of the cloud area is low compared with other areas, so band 10 has low values in cloud areas. Thin cloud was masked using band 1 in addition used to bands 9 and 10. Thin cloud, such as haze, has a small amount of ice in the cloud area, so that the sunlight passes through and reaches the land surface area; therefore, it usually has a low pixel value in band 9. In addition, thin cloud temperature is low, which means lower pixel values in band 10. Clouds are also normally brighter than other features, especially in the blue band, which means higher pixel values in band 1. The conditions for threshholding of each process of cloud masking are shown in table 2. Table 2. Thick and thin cloud mask threshold conditions Type of cloud mask Threshold conditions Thick cloud Band 9 > threshold B9 & Band 10 < threshold B10 Thin cloud Band 1 > threshold B1 & Band 9 < threshold B9 & Band 10 < threshold B10 Shadow masks A cloud and its shadow are not usually far apart. Analysis of experimental images revealed that the longest distance between a cloud and its shadow was less than 200 pixels. For this reason, a 200-pixel buffer zone was generated around cloud masks. We then examined the spectral information of experimental images to find a unique property of the cloud-shadows. The shadow areas were found to have lower pixel values in band 6 (NIR) compared with values of other features. In addition, we assumed that cloud-shadow could form one of the small normalized groups in an image. Otsu s N thresholding method, which has N-1 thresholds, was applied to the results; however, the fundamentals are same as those of Otsu s thresholding (Deepa and Subbiah Bharathi, 2013). Based on this algorithm, the experimental images were divided by three thresholds (N=3) to generate four segment groups. The first group, which had the lowest pixel value, was assigned as the cloud shadow group.

4 Cloud and shadow filling Pixels have a similar spectrum value in an image if the land-cover type is same. Suming (2013) used this concept, based on a SSG, to fill in the mask areas. His procedure is composed of three steps. First, the correspondence points are found for cloud and its shadow masks in the reference image. Second, the SSG location of first step s result is found in the reference image, and the locations of the SSG are transferred to the experimental images. The mean values are then used to replace the mask areas. The results are good, especially for geometric recovery; however, some color discordance was evident in the replaced points. For this reason, we used the SSG method for RGB bands, and added spectrum information for each band. Values of 0.95 and 1.2 were multiplied to the bands 2 (Blue) and 4 (Red), respectively. Before filling in the mask areas, a 10-pixel buffer was applied around the masking areas because the cloud and shadow masks are not detected perfectly near the cloud and shadow edges. RESULTS Figure 3 illustrates the result of cloud detection and masking, including the result of thick cloud and thin cloud mask areas. The process of thick cloud masking shows that most clouds were detected. Thin cloud masking indicated that a small amount of cloud was detected because the experimental images are composed of a large number of cirrus clouds, which contain water vapor. If an image has a great deal of haze, this thin cloud detection algorithm will play an important role in detecting clouds. Figure 3. Cloud masks (a) P38/R35, (b) P28/R45 The result of shadow detection and masking is shown in Figure 4. Shadow areas were excessively detected in some areas, especially small piece of clouds, but the overall result was good. The excessively detected area is not a significant problem because it is an extremely small area it will be also filled at the cloud filling step. In order to fill the mask area is filled in by generating the final cloud and shadow mask areas by integrating the cloud and shadow mask images (figure 5).

5 Figure 4. Shadow masks (a) P38/R35, (b) P28/R45 Figure 5. Final integrated cloud and shadow masks (a) P38/R35, (b) P28/R45 Figure 6 shows the results of generating cloud-free images. The masking areas were replaced by the proposed filling method. We reduced color distortion by multiplying the coefficient at the mean value, which is the output of the filling method. The cloud area was not perfectly replaced in both images, but it generated good results without geometric and color distortions.

6 Figure 6. Experiment (left) and cloud and shadow filled (right) images (a) P38/R35, (b) P28/R45 DISCUSSION & CONCLUSION The proposed method automatically detected and masked clouds and shadow area using Otsu s thresholding method. The mask areas were then filled using coordinates of the reference image and pixel values of experimental image, which corresponded to the coordinates of the reference image. The result was replacement of the cloud cover and shadow area were naturally replaced. It is a simple and automatic method that can be used in any kind of cloud cover with Landsat-8 images. This process will be helpful as an image preprocessing tool before the research involving land-cover classification and change detection using Landsat-8. Future research should focus on improving the filling methods for the cloud and shadow area, and evaluating the results by various accuracy assessment methods instead of visual methods. ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012R1A2A2A ). REFERENCES [1] E.H. Helmer and B. Ruefenacht, 2005, Cloud-Free Satellite Image Mosaics with Regression Trees and Histogram Matching, Photogrammetric Engineering & Remote Sensing, 9 (71), pp [2] K. D. HUTCHISON, N. J. CHOE, 1996, Application of 1-38 μm imagery for thin cirrus detection in daytime imagery collected over land surfaces, International Journal of Remote Sensing, 17

7 (17), pp Prianto E., 2007, Indonesian landed house electricity consumption, Home Design Going Green seminar, WWF-Indonesia, Jakarta. [3] Ma Ying-zhao, JIAO Wei-li, WANG Dui-zhou, LONG Teng-fei, Wang Wei, 2010, An Integrated Method To Generate A Cloud-free Image Automatically Based On Landsat5 Data, 2010 International Conference on Intelligent Computation Technology and Automation, Changsha, China. [4] Min LI, Soo Chin LIEW and Leong Keong KWOH, 2002, Generating Cloud Free and Cloud- Shadow Free Mosaic for SPOT Panchromatic Images, 2002 IEEE International Geoscience and Remote Sensing Symposium, Westin Harbour Castle, Toronto Canada, (4), [5] NOBUYUKI OTSU, 1975, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on System, 1 (9), pp [6] S.Deepa and Dr.V.Subbiah Bharathi, 2013, Efficient ROI segmentation of Digital Mammogram images using Otsu s N thresholding method, Indian Journal of Automation and Artifical Intelligence, 2 (1), pp [7] Suming Jin, Collin Homer, Limin Yang, George Xian, Joyce Fry, Patrick Danielson, and Philip A. Townsend, 2013, Cloud-Free Satellite Image Mosaics with Regression Trees and Histogram Matching, International Journal of Remote Sensing, 5 (34), pp [8] Xiaolin Zhu, Feng Gao, Desheng Liu, and Jin Chen, 2012, A Modified Neighborhood Similar Pixel Interpolator Approach for Removing Thick Clouds in Landsat images, IEEE Geoscience and Remote sensing, 3 (9), pp [9] Yi-Shiang Shiua, Meng-Lung Lin, Tzu-How Chu, 2011, Mapping and Recovering Cloud- Contaminated Area In Multispectral Satellite Imagery With Visible And Near-Infrared Bands, 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, pp

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