Application Study on Double-Constrained Change Detection for Land Use/Land Cover Based on GF-6 WFV Imageries
Application Study on Double-Constrained Change Detection for Land Use/Land Cover Based on GF-6 WFV Imageries
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DOI：
10.3390/rs12182943
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Jingxian Yu;Yalan Liu;Yuhuan Ren;Haojie Ma;Dacheng Wang;Yafei Jing;Linjun Yu
中科院分区：
文献类型：
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作者：
Jingxian Yu;Yalan Liu;Yuhuan Ren;Haojie Ma;Dacheng Wang;Yafei Jing;Linjun Yu
GF-6宽视场（WFV）作为高分（GF）系列的新一代卫星敏感器，分辨率为16 m，具有覆盖范围广、成像频率高的特点，与GF-1 WFV相比，增加了红边、黄边和紫边两个新波段。为检验GF-6 WFV数据补充波段在土地利用/土地覆盖变化检测中的有效性，采用了双约束变化检测方法（DCDM），该方法利用变化矢量强度和相关系数两个约束条件进行地物级变化检测。根据2018年6月和2019年6月在雄安新区采集的两幅GF-6 WFV影像，首先进行特征分析，以确定新波段是否有助于检测LULC的变化。然后，通过耦合这些选择的特征，变化向量的强度和相关系数作为双重约束，执行变化检测。研究表明，利用红边波段的相关特征进行LULC变化检测的总体准确率可达89%，与利用相应的时间型GF-1 WFV数据相比，提高了2%，而紫边和黄边波段的相关特征不能提供足够的有效信息。本研究为GF-6 WFV数据产品在变化检测领域的深入应用提供了理论支持，并探索了其在资源环境监测中的适用性和潜力，有助于其进一步应用。
As a new satellite sensor of the GaoFen (GF) series, GF-6 Wide Field of View (WFV) with the resolution of 16 m has the characteristics of wide coverage, high-frequency imaging and has four new bands of two red-edge, yellow, and purple compared with GF-1 WFV. In order to test the validity of the supplementary bands of GF-6WFV data for change detection of land use/land cover (LULC), this study applied the Double-constrained Change Detection Method (DCDM) that uses the double constraints (change vector intensity and correlation coefficient) for change detection on object-level. According to two GF-6WFV imageries acquired in the Xiong’an New Area in June of 2018 and 2019, feature analysis was performed to determine whether the new bands are helpful to detect the change of LULC first. Then, by coupling these selected features, the intensity of change vector and correlation coefficient were used as the double constraints to perform the change detection. The study demonstrates that the relevant features of the two red-edge bands can achieve the overall accuracy of 89% for change detection of LULC and improved by 2% comparing with using the corresponding temporal GF-1WFV data, while the purple and yellow bands cannot provide enough effective information for this detection. This study can provide theoretical support for the in-depth applications of GF-6 WFV data products in the change detection fields and has explored its applicability and potential in resource and environment monitoring, it is helpful to the further applications.