This paper presents a thresholding method for image segmentation by using an improved thresholding output function on
a two-dimensional (2-D) histogram based on Tsallis-Havrda-Charvat entropy principle. The Tsallis-Havrda-Charvat
entropy is obtained from two-dimensional histogram which has determined by using the gray value of the pixels and the
local average gray value of the pixels. Based on Tsallis-Havrda-Charvat entropy, we obtain the optimal threshold pair by
maximizing the criterion function. The threshold pair groups the projection drawing of the 2-D histogram into four
quadrants. Then we draw a line passing the optimal point. According to the line, we use the improved thresholding
output function to separate the four quadrants into two parts, above the line and below the line. Therefore, the pixels are
also grouped into two groups, targets and background. Experiment results show that the proposed method is robust to
noise.
This study employs SeaWiFS data over the waters off the southeastern China to evaluate a semi-analytical algorithm for
euphotic zone depth (Ze). This algorithm is based on water's inherent optical properties (IOPs), which can be
near-analytically calculated from spectral remote-sensing reflectance, where remote-sensing reflectance can be derived
from the normalized water-leaving radiance provided by SeaWiFS. In the Taiwan Strait, compared with in situ Ze (±3
hour within SeaWiFS collection), average error (ε) is 15.0 % and root mean square error (RMSE) is 0.074, with Ze in a
range of 14-34 m from field measurements. In the South China Sea, compared with in situ Ze (±48 hour within SeaWiFS
collection),ε is 5.1 % in summer and 22.6 in winter, while RMSE is 0.032 in summer and 0.129 in winter, with Ze in a
range of 10-82 m from field measurements. For comparison, we also evaluate the performance of the empirical Ze
algorithm that is based on chlorophyll concentration. It is found that the IOP-centered approach has higher accuracy
compared to the chlorophyll-a centered approach (e.g. in the South China Sea in winter, ε is 55.3 % and RMSE is 0.219).
The new algorithm is thus found not only worked well with waters of the Gulf of Mexico, Monterey Bay and the Arabian
Sea, but also worked well with waters of the China Sea.
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