Cloud Extraction from Chinese High Resolution Satellite Imagery by Probabilistic Latent Semantic Analysis and Object-Based Machine Learning

Automatic cloud extraction from satellite imagery is a vital process for many applications in optical remote sensing since clouds can locally obscure the surface features and alter the reflectance. Clouds can be easily distinguished by the human eyes in satellite imagery via remarkable regional characteristics, but finding a way to automatically detect various kinds of clouds by computer programs to speed up the processing efficiency remains a challenge. This paper introduces a new cloud detection method based on probabilistic latent semantic analysis (PLSA) and object-based machine learning. The method begins by segmenting satellite images into superpixels by Simple Linear Iterative Clustering (SLIC) algorithm while also extracting the spectral, texture, frequency and line segment features. Then, the implicit information in each superpixel is extracted from the feature histogram through the PLSA model by which the descriptor of each superpixel can be computed to form a feature vector for classification. Thereafter, the cloud mask is extracted by optimal thresholding and applying the Support Vector Machine (SVM) algorithm at the superpixel level. The GrabCut algorithm is then applied to extract more accurate cloud regions at the pixel level by assuming the cloud mask as the prior knowledge. When compared to different cloud detection methods in the literature, the overall accuracy of the proposed cloud detection method was up to 90 percent for ZY-3 and GF-1 images, which is about a 6.8 percent improvement over the traditional spectral-based methods. The experimental results show that the proposed method can automatically and accurately detect clouds using the multispectral information of the available four bands.

[1]  Gülsen Taskin Kaya A Hybrid Model for Classification of Remote Sensing Images With Linear SVM and Support Vector Selection and Adaptation , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Qing Zhang,et al.  Cloud Detection of RGB Color Aerial Photographs by Progressive Refinement Scheme , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Maoteng Zheng,et al.  On-Orbit Geometric Calibration of ZY-3 Three-Line Array Imagery With Multistrip Data Sets , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Michael D. King,et al.  Comparison of near‐infrared and thermal infrared cloud phase detections , 2006 .

[5]  Adrian Fisher,et al.  Cloud and Cloud-Shadow Detection in SPOT5 HRG Imagery with Automated Morphological Feature Extraction , 2014, Remote. Sens..

[6]  Perry Xiao,et al.  In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). , 2014, International journal of pharmaceutics.

[7]  Xian-Jun Gao,et al.  [Real-time automatic cloud detection during the process of taking aerial photographs]. , 2014, Guang pu xue yu guang pu fen xi = Guang pu.

[8]  Wenzhong Shi,et al.  Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm , 2016, Remote. Sens..

[9]  M. A. Aguilar,et al.  Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses , 2008 .

[10]  Yongjun Zhang,et al.  Automatic Processing of Chinese GF-1 Wide Field of View Images , 2015 .

[11]  Xuemei Xu,et al.  Cloud image detection based on Markov Random Field , 2012 .

[12]  Liu Jian,et al.  I MPROVEMENT OF DYNAMIC THRESHOLD VALUE EXTRACTION TECHNIC IN FY-2 CLOUD DETECTION , 2010 .

[13]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[14]  Xinwu Li,et al.  A Robust Approach for Object-Based Detection and Radiometric Characterization of Cloud Shadow Using Haze Optimized Transformation , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Liangpei Zhang,et al.  A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation , 2014, Remote. Sens..

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

[18]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Nikos Koutsias,et al.  SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

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

[21]  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 .

[22]  R. Srinivasan,et al.  An automated cloud detection method for daily NOAA-14 AVHRR data for Texas, USA , 2002 .

[23]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Yun Zhang,et al.  Wavelet-based image registration technique for high-resolution remote sensing images , 2008, Comput. Geosci..

[26]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[27]  Xiangyun Hu,et al.  Bag-of-Words and Object-Based Classification for Cloud Extraction From Satellite Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[29]  Ming Zhang,et al.  A neutrosophic approach to image segmentation based on watershed method , 2010, Signal Process..

[30]  Liangpei Zhang,et al.  An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[32]  Liangpei Zhang,et al.  Combining Pixel- and Object-Based Machine Learning for Identification of Water-Body Types From Urban High-Resolution Remote-Sensing Imagery , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Anne Puissant,et al.  Optimizing Spatial Resolution of Imagery for Urban Form Detection - The Cases of France and Vietnam , 2011, Remote. Sens..

[34]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[35]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[36]  Yan-Ting Liau,et al.  Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California , 2014, Remote. Sens..