A New Land Cover Classification Method Using Grade-Added Rough Sets
A New Land Cover Classification Method Using Grade-Added Rough Sets
复制标题
一种使用分级粗糙集的新土地覆盖分类方法
DOI：
10.1109/lgrs.2020.2965297
复制
发表时间：
2020-01
影响因子：
4.8
通讯作者：
Kinoshita Tsuguki
中科院分区：
文献类型：
--
作者：
Ishii Yoshie;Bagan Hasi;Iwao Koki;Kinoshita Tsuguki
最近，使用粗糙集理论进行土地覆盖分类取得了重大进展，导致制作高度准确的地图。然而，通过数字（DN）的离散化可能发生信息丢失，并且可能需要额外的努力来确定参数设置。此外，以往的研究还没有明确基于粗糙集理论的土地覆盖分类的特点。针对现有土地覆被分类方法中存在的问题，提出了一种新的基于粗糙集理论的土地覆被分类方法-加级粗糙集（GRS）方法，并对GRS作为一种典型的土地覆被分类方法的特点进行了研究。通过考虑坡度，GRS避免了离散化过程中的信息损失，并且不需要设置任何参数。为了评估所提出的GRS，进行了三个实验。首先，GRS的准确性与经典粗糙集（CRS），最大似然分类器（MLC），和支持向量机（SVM）进行了比较。其他实验分别研究了分类准确率对类别类定义和训练数据选择的敏感性。GRS被认为是准确的现有的分类方法和更强大的比MLC和SVM的类别类别定义和选择的训练数据。这些结果意味着可以定义类别而不考虑基于光谱反射特性的分类的难易程度，从而减轻了用户的负担，并且分类结果具有高度的可靠性，因为它们独立于训练数据。
Recently, the use of a rough set theory for land cover classification has progressed significantly, leading to the production of highly accurate maps. However, information loss can occur through the discretization of digital numbers (DNs), and an additional effort may be required to determine the parameter settings. Furthermore, previous studies have not clarified the characteristics of land cover classification based on the rough set theory. This letter develops a new method of grade-added rough sets (GRS) to solve the problems of the existing land cover classifications employing the rough set theory and investigates the characteristics of GRS as a representative land cover classification method. By considering the grade, GRS prevents information loss from discretization and does not require any parameters to be set. To assess the proposed GRS, three experiments were conducted. First, the accuracy of GRS was compared with that of classical rough sets (CRSs), a maximum likelihood classifier (MLC), and a support vector machine (SVM). The other experiments investigated the sensitivity of the classification accuracy with respect to the category class definitions and selection of training data, respectively. GRS was found to be as accurate as existing classification methods and more robust than both MLC and SVM in terms of the category class definitions and selection of training data. These results imply that the classes can be defined without considering the ease of classification based on spectral reflection characteristics, thus reducing the burden on users, and the classification results have a high degree of reliability because they are independent of the training data.
登录
查看更多内容
DOI：
10.1007/978-3-319-99368-3
发表时间：
2018-10
期刊：
--
影响因子：
--
作者：
R. Efendi;Voni Apriana Dewi;Rahmadeni;Sri Basriati;Dadang Syarif
通讯作者：
R. Efendi;Voni Apriana Dewi;Rahmadeni;Sri Basriati;Dadang Syarif
影响因子：
13.5
作者：
Khatami, Reza;Mountrakis, Giorgos;Stehman, Stephen V.
通讯作者：
Stehman, Stephen V.
影响因子：
3.4
作者：
Ouyang Yun;Jianwen Ma
通讯作者：
Ouyang Yun;Jianwen Ma
DOI：
--
发表时间：
1996-08
期刊：
--
影响因子：
--
作者：
S. Tsumoto;Hiroshi Tanaka
通讯作者：
S. Tsumoto;Hiroshi Tanaka
DOI：
10.1007/978-94-011-3534-4
发表时间：
1991-10
期刊：
--
影响因子：
--
作者：
Z. Pawlak
通讯作者：
Z. Pawlak