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Built-up area extraction using Landsat 8 OLI imagery

机译：使用Landsat 8 OLI图像提取建筑面积

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摘要

The normalized difference built-up index (NDB1) has been useful for mapping urban built-up areas using Landsat Thematic Mapper (TM) data. The applicability of this index to the newer Landsat-8 Operational Land Imager (OLI) data was examined during this study, and a new method for built-up area extraction has been proposed. OLI imagery of urban areas of Lahore, Pakistan, was used to extract built-up areas through a modified NDBI approach and the proposed built-up area extraction method (BAEM). Instead of using individual bands, BAEM employed principal component analysis images of the highly correlated bands pertinent to NDBI computation. Through integration of temperature data, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), BAEM was able to improve the overall accuracy of built-up area extraction by 11.84% compared to the modified NDBI approach. Rather than employing the binary NDBI, NDVI and MNDWI images, continuous images of these indices were used, and the final output was recoded by determining the threshold value through a double-window flexible pace search (DFPS) method. Results indicate that BAEM was more accurate at mapping urban built-up areas when applied to OLI imagery as compared to the modified NDBI approach; omission and commission errors were reduced by 75.96% and 33.36%, respectively. Moreover, the use of DFPS improved robustness of the proposed approach by enhancing user control over the segmentation of the output.
机译：归一化差异建筑指数（NDB1）对于使用Landsat Thematic Mapper（TM）数据绘制城市建筑区域非常有用。在这项研究期间，研究了该指数对更新的Landsat-8作战土地成像仪（OLI）数据的适用性，并提出了一种新的建筑面积提取方法。通过改进的NDBI方法和拟议的建筑面积提取方法（BAEM），使用巴基斯坦拉合尔市区的OLI图像来提取建筑面积。 BAEM而不是使用单个频段，而是使用与NDBI计算相关的高度相关频段的主成分分析图像。通过整合温度数据，归一化差异植被指数（NDVI）和修改后的归一化差异水指数（MNDWI），BAEM与修改后的NDBI方法相比，能够将建筑面积提取的总体精度提高11.84％。而不是使用二进制NDBI，NDVI和MNDWI图像，而是使用这些索引的连续图像，并通过双窗口灵活步速搜索（DFPS）方法确定阈值来对最终输出进行重新编码。结果表明，与改进的NDBI方法相比，当应用于OLI图像时，BAEM在绘制城市建成区方面更准确；遗漏和佣金错误分别减少了75.96％和33.36％。此外，DFPS的使用通过增强用户对输出分段的控制，提高了所提出方法的鲁棒性。

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