基于多传感器遥感数据和OBIA方法的石灰石矿区分类——以巴布亚Biak岛为例

2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2018-10-01 DOI:10.1109/ICACSIS.2018.8618198

Daniel Sande Bona, A. M. Arymurthy, P. Mursanto

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引用次数: 1

摘要

巴布亚比亚克岛的大部分土壤类型是珊瑚石灰岩。这种石灰石被用作建筑材料。石灰石开采是当地居民的收入来源之一。本研究尝试采用多传感器遥感数据融合和基于目标的图像分析(OBIA)分类方法绘制石灰石矿区地图。2015年和2017年获得的1.5 m分辨率SPOT-6数据作为OBIA分类过程中的光谱和几何参数。利用2017年11月至2018年5月Sentinel-IA SLC SAR数据的PS-InSAR技术获得的地表变形点作为OBIA分类过程的结构变量，确定矿区是否处于活动状态。2015年SPOT-6数据分类结果的总体准确率为84.7%，2017年SPOT-6数据分类结果的总体准确率为74.9%。

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Classification of Limestone Mining Site using Multi-Sensor Remote Sensing Data and OBIA Approach a Case Study: Biak Island, Papua

Most of Soil Type in Biak Island, Papua is Coral Limestone. This limestone is used as building material. Limestone mining is one of the income sources for local people. This study tries to map limestone mining sites using multi-sensor remote sensing data fusion and Object-Based Image Analysis (OBIA) classification approach. 1.5 meters resolution SPOT-6 data acquired in 2015 and 2017 used as spectral and geometric parameters in OBIA classification process. Surface deformation points obtained from the PS-InSAR technique on Sentinel-IA SLC SAR data acquired from November 2017 to May 2018 is used as the structural variable for OBIA classification process to determine whether mining site is active or inactive. The overall accuracy of classification result is 84.7% for 2015 SPOT-6 data and 74.9% for 2017 SPOT-6 data.

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2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)

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