基于土壤湿度水平的高级差分进化多级分割卫星图像

Meera Ramadas, A. Abraham
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引用次数: 1

摘要

土壤湿度有助于分析人员进行土壤科学、农业和水文学研究。估算土壤湿度的卫星图像是通过地球卫星记录的。通过基于土壤含水量的卫星图像分割,我们可以毫不费力地识别出湿润的区域和干燥的区域。差分进化(DE)是一种流行的进化方法,用于优化图像分割等问题。在这项工作中,引入了一种先进的差分进化(aDE)技术,与传统的差分进化方法相比,它具有更高的性能。该方法结合Renyi熵对图像进行多级分割。采用该方法得到的分割图像质量有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segregating Satellite Imagery Based on Soil Moisture Level Using Advanced Differential Evolutionary Multilevel Segmentation
Soil Moisture aid analysts in study of soil science, agriculture and hydrology. Satellite imagery for soil moisture estimation is recorded through earth satellites. By segmenting these satellite imageries based on soil moisture content, we can effortlessly identify regions of wetter condition and regions of dry condition. Differential evolution (DE) is a popular evolutionary approach that is used to optimize problems like image segmentation. In this work, an Advanced Differential Evolution (aDE) technique is introduced which has enhanced performance in comparison to traditional DE approach. This approach is combined with Renyi's entropy for performing multilevel segmentation on the imagery. The resultant segmented images obtained on using the proposed technique is of enhanced quality.
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