A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm

Cloud and cloud shadow detection is an important preprocess before using satellite images for different applications. It can be considered as a classification process, in which the objective pixels are partitioned into cloud/cloud shadow or non-cloud/non-cloud shadow classes. However, some cloud pixels, especially the thin cloud pixels, can be considered as a mixture of reflectances of clouds and land objects. In fuzzy clustering, the data points can belong to two or more clusters; hence, fuzzy clustering may better characterize the status of one given pixel belonging to clouds or non-clouds. The fuzzy c-means method (FCM), one typical fuzzy clustering method, was utilized in this study for cloud and cloud shadow detection. In addition, the “flood-fill” morphological transformation may misclassify some clear-sky areas surrounded by clouds as cloud shadows as a whole, so a modified cloud shadow index calculation was proposed. Moreover, a cloud and cloud shadow spatial matching strategy based on the projection direction and spatial coexistence was used to exclude some pseudo cloud shadows. Fewer predefined parameters and spectral bands are needed is one characteristic of the proposed method. In this study, 41 scenes including 27 Landsat ETM+ images in eight latitude zones and 14 Landsat OLI images comprising seven land cover types, including barren, forest, grass, shrubland, urban, water, and wetlands areas, with percentages of cloud cover from 4.99% to 97.63%, were utilized to confirm the validity of the FCM. The detected results demonstrate that the thick and thin clouds along with their associated cloud shadows can be precisely extracted by using the FCM. Compared with the function of mask (Fmask) method, the FCM has relatively lower producer agreement rates, but it misclassifies as clouds fewer clear-sky pixels; compared with the support vector machine (SVM) method, the FCM can achieve better cloud detection accuracy. The results demonstrate that the FCM can attain a better balance between cloud pixel detection and non-cloud pixel exclusion.

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

[2]  Quan Wang,et al.  A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data , 2018 .

[3]  Gustau Camps-Valls,et al.  Multitemporal Cloud Masking in the Google Earth Engine , 2018, Remote. Sens..

[4]  Yi Luo,et al.  Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America , 2008 .

[5]  Zhe Zhu,et al.  Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change , 2014 .

[6]  Jian Wang,et al.  A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths , 2017 .

[7]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[8]  B. He,et al.  Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery , 2019, Remote Sensing of Environment.

[9]  Zhe Zhu,et al.  Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .

[10]  Jun Zhou,et al.  Object Classification via Feature Fusion Based Marginalized Kernels , 2015, IEEE Geoscience and Remote Sensing Letters.

[11]  Gérard Dedieu,et al.  A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images , 2010 .

[12]  Paolo Gamba,et al.  Multi-feature combined cloud and cloud shadow detection in GF-1 WFV imagery , 2016, ArXiv.

[13]  R. Colombo,et al.  Carving and adaptive drainage enforcement of grid digital elevation models , 2003 .

[14]  Lei Wang,et al.  Fuzzy AutoEncode Based Cloud Detection for Remote Sensing Imagery , 2017, Remote. Sens..

[15]  C. Woodcock,et al.  Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images , 2015 .

[16]  Hsu-Yung Cheng,et al.  Multi-model solar irradiance prediction based on automatic cloud classification , 2015 .

[17]  Neil Flood,et al.  Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM/ETM+ time series , 2013 .

[18]  M. Joseph Hughes,et al.  Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing , 2014, Remote. Sens..

[19]  Yu Oishi,et al.  Development of a support vector machine based cloud detection method for MODIS with the adjustability to various conditions , 2018 .

[20]  John L. Dwyer,et al.  Development of the Landsat Data Continuity Mission Cloud-Cover Assessment Algorithms , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Yong Zhang,et al.  Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction , 2016 .

[22]  Deren Li,et al.  Cloud Detection for High-Resolution Satellite Imagery Using Machine Learning and Multi-Feature Fusion , 2016, Remote. Sens..

[23]  Jin Chen,et al.  A Novel Cloud Removal Method Based on IHOT and the Cloud Trajectories for Landsat Imagery , 2018, Remote. Sens..

[24]  Y. Kaufman,et al.  Selection of the 1.375-µm MODIS Channel for Remote Sensing of Cirrus Clouds and Stratospheric Aerosols from Space , 1995 .

[25]  Chengquan Huang,et al.  Multi-temporal cloud and snow detection algorithm for the HJ-1A/B CCD imagery of China , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[26]  Xiaolin Zhu,et al.  An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions , 2018, Remote Sensing of Environment.

[27]  Bo Yi Lin,et al.  Radiometric normalization and cloud detection of optical satellite images using invariant pixels , 2015 .

[28]  Sylvie Le Hégarat-Mascle,et al.  Use of Markov Random Fields for automatic cloud/shadow detection on high resolution optical images , 2009 .

[29]  Yan Wang,et al.  Automatic Recognition of Cloud Images by Using Visual Saliency Features , 2015, IEEE Geoscience and Remote Sensing Letters.

[30]  S. Goward,et al.  Characterization of the Landsat-7 ETM Automated Cloud-Cover Assessment (ACCA) Algorithm , 2006 .

[31]  Pengfei Li,et al.  A cloud image detection method based on SVM vector machine , 2015, Neurocomputing.

[32]  Zhe Zhu,et al.  Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images , 2017 .

[33]  Y. Kaufman,et al.  Corection of thin cirrus path radiances in the 0.4–1.0 μm spectral region using the sensitive 1.375 μm cirrus detecting channel , 1998 .

[34]  Ping Yang,et al.  An algorithm using visible and 1.38-μm channels to retrieve cirrus cloud reflectances from aircraft and satellite data , 2002, IEEE Trans. Geosci. Remote. Sens..

[35]  A. Lacis,et al.  Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data , 2004 .

[36]  Collin G. Homer,et al.  Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA , 2013 .

[37]  Dengfeng Chai,et al.  Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks , 2019, Remote Sensing of Environment.

[38]  Jin Chen,et al.  An Iterative Haze Optimized Transformation for Automatic Cloud/Haze Detection of Landsat Imagery , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Shutao Li,et al.  A Coarse-to-Fine Method for Cloud Detection in Remote Sensing Images , 2019, IEEE Geoscience and Remote Sensing Letters.

[40]  Mohinder Malhotra.  Single Image Haze Removal Using Dark Channel Prior , 2016 .

[41]  Xuelong Li,et al.  General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.