Method of urban land change detection that is based on GF-2 high-resolution RS images

IF 1.8 Q3 REMOTE SENSING

International Journal of Image and Data Fusion Pub Date : 2020-11-30 DOI:10.1080/19479832.2020.1845246

Zhongbin Li, Ping Wang, M. Fan, Yifan Long

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引用次数: 3

Abstract

ABSTRACT With the successful launch of China’s high spatial resolution satellite Gaofen-2 (GF-2), the use of high spatial resolution satellite images for land change detection has high research potential. Based on the images from GF-2, this study combines principal component analysis and the spectral feature change method to identify different land changes in the form of different coloured patches. Then, three decision tree classification models are constructed to automatically detect the change, which includes information on the increase in the number of airports and buildings and increased or decreased vegetation. Further, through Quick Bird images for identical regions in the same periods, a sample of 2624 pixels is selected using a stratified random sampling method to verify the accuracy of the results indicating a change. The results show that the overall accuracy of the extracted information on land change was 98.21%, and the Kappa coefficient was 0.9604. Therefore, the method for land change detection and extraction of land change information used in this study is proven to be effective.

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基于GF-2高分辨率遥感影像的城市土地变化检测方法

摘要随着中国高空间分辨率卫星高分二号的成功发射，利用高空间分辨率的卫星图像进行土地变化探测具有很高的研究潜力。基于GF-2的图像，本研究将主成分分析和光谱特征变化方法相结合，以不同颜色斑块的形式识别不同的土地变化。然后，构建三个决策树分类模型来自动检测变化，其中包括机场和建筑物数量增加以及植被增加或减少的信息。此外，通过在相同周期内的相同区域的Quick Bird图像，使用分层随机采样方法选择2624个像素的样本，以验证指示变化的结果的准确性。结果表明，提取的土地变化信息总体准确率为98.21%，Kappa系数为0.9604。因此，本研究中使用的土地变化检测和土地变化信息提取方法被证明是有效的。

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来源期刊

International Journal of Image and Data Fusion REMOTE SENSING-

CiteScore

5.00

自引率

0.00%

发文量

期刊介绍： International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).