Sampling approaches for one-pass land-use/land-cover change mapping
Sampling approaches for one-pass land-use/land-cover change mapping
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DOI：
10.1080/01431160903475399
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发表时间：
2010-02
影响因子：
3.4
通讯作者：
Zhi Huang;X. Jia;L. Ge
中科院分区：
文献类型：
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作者：
Zhi Huang;X. Jia;L. Ge
在土地利用/土地覆盖变化（LULCC）制图，培训领域的变化类往往很难确定。在这项研究中，提出了一种新的采样策略映射LULCCs，其中的变化和无变化的样本直接从重叠的双时相图像与一个小的移位或旋转。这样，所创造的人为变化保持了一定程度的地理信息。这种方法进行了比较与模拟采样的方法，其中每个土地利用/土地覆盖类型的训练样本分别从个人的图像，然后交叉组合，形成“从”类。案例研究表明，这两种采样策略减轻了执行一遍分类LULCC检测的困难，并产生更准确的LULCC地图比传统的两步分类后比较方法。在两种抽样策略中，所提出的一个提供了更高的测试精度比模拟抽样方法，而后者更容易实现。
In land-use/land-cover change (LULCC) mapping, training fields for changed classes are often difficult to identify. In this study, a new sampling strategy is proposed for mapping LULCCs, in which change and no-change samples are obtained directly from overlaid bi-temporal images with a small shift or rotation. In this way, the artificial changes created maintain a certain degree of geographic information. This method is compared with a simulated sampling approach in which training samples of each land-use/land-cover type are selected separately from individual images and then cross-combined to form the ‘from–to’ classes. The case study demonstrates that both sampling strategies ease the difficulties in performing one-pass classification for LULCC detection and yield more accurate LULCC maps than that of the traditional two-step post-classification comparison method. Between the two sampling strategies, the proposed one provides higher testing accuracy than the simulated sampling approach, while the latter is easier to implement.