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Formosat-2 image is a kind of high-spatial-resolution (2 meters GSD) remote sensing satellite data, which includes one
panchromatic band and four multispectral bands (Blue, Green, Red, near-infrared). An essential sector in the daily
processing of received Formosat-2 image is to estimate the cloud statistic of image using Automatic Cloud Coverage
Assessment (ACCA) algorithm. The information of cloud statistic of image is subsequently recorded as an important
metadata for image product catalog. In this paper, we propose an ACCA method with two consecutive stages: preprocessing
and post-processing analysis. For pre-processing analysis, the un-supervised K-means classification, Sobel’s
method, thresholding method, non-cloudy pixels reexamination, and cross-band filter method are implemented in
sequence for cloud statistic determination. For post-processing analysis, Box-Counting fractal method is implemented. In
other words, the cloud statistic is firstly determined via pre-processing analysis, the correctness of cloud statistic of
image of different spectral band is eventually cross-examined qualitatively and quantitatively via post-processing
analysis. The selection of an appropriate thresholding method is very critical to the result of ACCA method. Therefore,
in this work, We firstly conduct a series of experiments of the clustering-based and spatial thresholding methods that
include Otsu’s, Local Entropy(LE), Joint Entropy(JE), Global Entropy(GE), and Global Relative Entropy(GRE) method,
for performance comparison. The result shows that Otsu’s and GE methods both perform better than others for
Formosat-2 image. Additionally, our proposed ACCA method by selecting Otsu’s method as the threshoding method has
successfully extracted the cloudy pixels of Formosat-2 image for accurate cloud statistic estimation.
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