CAJ | 학술논문

针对对像元尺度上独立于分类方法的不确定性评价的需要和对数混合熵函数在评价遥感影像分类不确定性中存在的不足,该文提出了一种基于参数型指数混合熵模型的农业遥感影像分类不确定性评价方法.研究首先对指数混合熵函数进行改进,推导出参数型指数混合熵函数并确定出适合于评价农作区遥感影像分类的参数;然后,使用该函数建立一种像元尺度上独立于分类的不确定性评价模型;最后,将该模型应用于空间分辨率退化10倍的 SPOT-5影像中,并使用原始影像对评价结果进行验证.试验结果表明,当模型中参数型指数混合熵函数的参数分别为4和1时,该函数比对数混合熵函数更好地统一了模糊性和随机性,熵值范围提高了2.11倍.该模型不确定性评价结果与原始影像3种分类的不确定像元比例相关系数分别为0.60、0.66、0.70,评价结果较为准确.因此,该模型可以在像元尺度上独立于分类方法将地物类别相对复杂的农业遥感影像分类不确定性更为精确地表达出来,为确保农作物种植面积提取、区域产量遥感估测精度提供了有力支撑.
침대대상원척도상독립우분류방법적불학정성평개적수요화대수혼합적함수재평개요감영상분류불학정성중존재적불족,해문제출료일충기우삼수형지수혼합적모형적농업요감영상분류불학정성평개방법.연구수선대지수혼합적함수진행개진,추도출삼수형지수혼합적함수병학정출괄합우평개농작구요감영상분류적삼수;연후,사용해함수건립일충상원척도상독립우분류적불학정성평개모형;최후,장해모형응용우공간분변솔퇴화10배적 SPOT-5영상중,병사용원시영상대평개결과진행험증.시험결과표명,당모형중삼수형지수혼합적함수적삼수분별위4화1시,해함수비대수혼합적함수경호지통일료모호성화수궤성,적치범위제고료2.11배.해모형불학정성평개결과여원시영상3충분류적불학정상원비례상관계수분별위0.60、0.66、0.70,평개결과교위준학.인차,해모형가이재상원척도상독립우분류방법장지물유별상대복잡적농업요감영상분류불학정성경위정학지표체출래,위학보농작물충식면적제취、구역산량요감고측정도제공료유력지탱.
Uncertainty is the most important factor which affects the quality of remote sensing image classification (RSIC), research on uncertainty in RSIC is a cutting-edge, hot topic in remote sensing application study. Study of RSIC gradually developed from simple qualitative and non-positioning research into specific quantitative and positioning research. At present, a RSIC uncertainty evaluation model based on pixel scale and independent of the classification method should be established. In recent years, some scholars began to use hybrid entropy model to evaluate uncertainty in RSIC. However, these studies did not focus on a particular area and find out a suitable entropy function. How to find out a suitable entropy function which better integrate both fuzziness and randomness and facilitate a wider range of entropy values has always been a difficult point of research. From the discussion above, this paper established a method for evaluating uncertainty in agricultural RSIC based on exponential hybrid entropy in parametric form (EHEP). In this study, firstly, the exponential hybrid entropy function was deduced in parametric form, and EHEP was obtained. EHEP is improvement of hybrid entropy which has the shortcoming of lacking adjustable parameters. After adjusting parameters, entropy function can better integrate fuzziness and randomness and facilitate a wider range of entropy values, so this function is suitable for evaluating RSIC uncertainty. Moreover, by the research on the relationship between the parameters and the entropy function surface, the paper ascertained parameters which are suitable for evaluating uncertainty in farming area RSIC. Secondly, EHEP was used to establish a RSIC uncertainty evaluation model based on pixel scale and independent of the classification method, in order to offer elicitation to simulation of the uncertainty transferred in space model, and to help fill a vacancy in uncertainty evaluation model based on pixel scale and independent of the classification method. Lastly, the EHEP model was used to test and verify in SPOT-5 image of Zhenlai County, Jilin Province. The results indicate that in EHEP when parameters are equivalent to 4 and 1, respectively, the function better integrates fuzziness with randomness, and increases entropy value range by 2.11 times compared with logarithmic hybrid entropy function. In addition, the EHEP model evaluates contribution of different pixels to the uncertainty based on pixel scale and independent of the classification method, and corrects deficiency of error matrix in evaluation of RSIC accuracy. Furthermore, it visually expresses the uncertainty, contributes to the overall mastery of RSIC uncertainty’s value, distribution, spatial structure and trend, and locates the coordinates of the area where uncertainty exists. Therefore, the EHEP model can make more accurate expression of the uncertainty in agricultural RSIC with relatively complex objects based on pixel scale and independent of the classification method, effectively bolstering precision of crop planting area extraction and remote sensing-based regional yield estimation.