Cloud Detection for PERUSAT-1 Imagery Using Spectral and Texture Descriptors, ANN, and Panchromatic Fusion

  • Giorgio MoralesEmail author
  • Samuel G. Huamán
  • Joel Telles
Conference paper


The cloud detection process is a prerequisite for many remote sensing applications in order to use only those cloud-free parts of satellite images and reduce errors of further automatic detection algorithms. In this paper, we present a method to detect clouds in high-resolution images of 2.8 m per pixel approximately. The process is performed over those pixels that exceed a defined threshold of blue normalized difference vegetation index to reduce the execution time. From each pixel, a set of texture descriptors and reflectance descriptors are processed in an Artificial Neural Network. The texture descriptors are extracted using the Gray-Level Co-occurrence Matrix. Each detection result passes through a false-positive discard procedure on the blue component of the panchromatic fusion based on image processing techniques such as Region growing, Hough transform, among others. The results show a minimum Kappa coefficient of 0.80 and an average of 0.94 over a set of 25 images from the Peruvian satellite PERUSAT-1, operational since December 2016.


Cloud detection High-resolution Artificial neural networks Texture analysis 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Institute of Research and Training at Telecommunications (INICTEL-UNI), National University of EngineeringLimaPeru

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