Discussion on classification methods of urban features based on GF-2 images

doi:10.13474/j.cnki.11-2246.2019.0210

Abstract

Abstract: The GF-2 image has higher resolution as well as more detailed characteristics of spectral, features, geometric and texture. In order to explore the classification method of the GF-2 image in urban features, an object oriented classification method based on optimal scale and rules is proposed in the study area of Longchang County, Sichuan Province. Based on segmentations, the evaluation function is constructed, combining with the maximum area method, optimal segmentation scales are selected to construct multiple layers. The spectral, geometric and texture features of the image are extracted to establish rules for classification and compared with the classification methods of object-oriented with the single scale and pixel based. The results show that the overall accuracy and Kappa coefficient of the proposed method are 93.33% and 0.92, respectively.

Key words: GF-2 image, object-oriented, multi-scale segmentation, classification rule, urban features

CLC Number:

P237

WANG Fang, YANG Wunian, WANG Jian, XIE Bing, YANG Xin, REN Jintong. Discussion on classification methods of urban features based on GF-2 images[J]. 测绘通报, 2019, 0(7): 12-16.

References

[1] 卓娜. 基于高分二号面向对象的城市地物识别方法研究[D]. 北京:中国地质大学, 2017.
[2] 王芳,杨武年,邓晓宇,等. 高分二号数据的城市生态用地分类方法探讨[J]. 测绘科学, 2018, 43(3):71-76.
[3] CHEN G, WENG Q, HAY G J, et al. Geographic object-based image analysis (GEOBIA):emerging trends and future opportunities[J]. Giscience & Remote Sensin, 2018, 55(2):159-182.
[4] 杨朝斌,张树文,卜坤,等. 高分辨率遥感影像在城市LUCC中的应用[J]. 中国科学院大学学报, 2016, 33(3):289-297.
[5] 苏伟,李京,陈云浩,等. 基于多尺度影像分割的面向对象城市土地覆被分类研究——以马来西亚吉隆坡市城市中心区为例[J]. 遥感学报, 2007, 11(4):521-530.
[6] 冯文卿,眭海刚,涂继辉,等. 高分辨率遥感影像的随机森林变化检测方法[J]. 测绘学报, 2017, 46(11):1880-1890.
[7] 李伟. GF-2遥感影像城市空间信息提取与应用研究[D]. 北京:中国地质大学, 2017.
[8] 何顺兵,牟凤云. 基于面向对象的多尺度山地城市土地覆盖信息提取——以重庆市大学城城区为例[J]. 重庆工商大学学报(自然科学版), 2016, 33(1):83-88.
[9] MYINT S W, GOBER P, BRAZEL A, et al. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery[J]. Remote Sensing of Environment, 2011, 115(5):1145-1161.
[10] BHASKARAN S, PARAMANANDA S, RAMNARAYAN M. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data[J]. Applied Geograph, 2010, 30(4):650-665.
[11] 李朝奎,方文,董小姣. 面向对象和规则的高分辨率影像分类研究[J]. 测绘通报, 2015(9):9-13.
[12] 王蕾,杨武年,任金铜,等. GF-2影像面向对象典型城区地物提取方法[J]. 测绘通报, 2018(1):138-142.
[13] 李晓靖,彭道黎,王海宾. 基于最优尺度和规则的高分辨率影像分类研究[J]. 测绘工程, 2017, 26(9):14-22.
[14] 袁泽. 基于GF-2遥感影像的城镇地物信息提取分割尺度研究[D]. 乌鲁木齐:新疆大学, 2016.
[15] 王彩艳,王瑷玲,王介勇,等. 基于面向对象的北京市区城市内部用地信息提取[J]. 自然资源学报, 2015, 30(4):705-714.