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Identification of Remote Sensing-Based Land Cover Types Combining Nearest-Neighbor Classification and SEaTH Algorithm
Journal of the Indian Society of Remote Sensing ( IF 2.5 ) Pub Date : 2020-07-01 , DOI: 10.1007/s12524-020-01131-6
Jinling Zhao , Yan Fang , Mingmei Zhang , Yingying Dong

The development of spaceborne remote sensing has greatly facilitated the land cover mapping at various spatial scales. Classification accuracy, however, is usually affected by the heterogeneous spectra of different land cover types for medium–low-spatial-resolution images. The study is aimed at improving the classification accuracy at a city scale by proposing a hierarchical classification method. Time-series Landsat-5 and Landsat-8 Operational Land Imager remote sensing images of 4 years were used as the classified images. A total of six first-class land cover types were determined, namely woodland, grassland, cropland, wetland, artificial surface and others. The object-based image analysis was chosen over pixel-based approaches. More specifically, the nearest-neighbor (NN) classification and SEparability and THresholds (SEaTH) algorithm were combined to produce a hierarchical classification method (NN-SEaTH). SEaTH algorithm was first used to extract the wetland after performing image segmentation in eCognition Developer. Then, the non-wetland was further classified to vegetation and non-vegetation by using a normalized difference vegetation index image. Finally, the other types were then obtained using the NN classification. To validate the proposed method, the NN classifier and NN-SEaTH method were compared. The proposed technique is shown to increase the overall accuracy (OA) and kappa coefficient (k) for the 4 years. The OA and k are, respectively, 96.46% and 0.9231, 96.63% and 0.9269, 96.88% and 0.9394, 95.22% and 0.9239 that are much larger than 88.13% and 0.7503, 88.83% and 0.7660, 88.64% and 0.7630, 87.33% and 0.7371 derived from the NN approach. The study provides a reference for medium-resolution-based land cover mapping by a hierarchical classification.

中文翻译:

基于遥感的土地覆盖类型识别结合最近邻分类和 SEaTH 算法

星载遥感的发展极大地促进了各种空间尺度的土地覆盖制图。然而,分类精度通常受到中低空间分辨率图像不同土地覆盖类型的异构光谱的影响。该研究旨在通过提出一种分层分类方法来提高城市范围内的分类精度。使用时间序列 Landsat-5 和 Landsat-8 Operational Land Imager 4 年的遥感影像作为分类影像。共确定林地、草地、农田、湿地、人工地表等6种一级土地覆盖类型。选择基于对象的图像分析而不是基于像素的方法。进一步来说,最近邻 (NN) 分类和 SEparability and THresholds (SEaTH) 算法相结合以产生分层分类方法 (NN-SEaTH)。SEaTH算法是在eCognition Developer中进行图像分割后首先用于提取湿地的。然后,利用归一化差异植被指数图像将非湿地进一步分类为植被和非植被。最后,然后使用 NN 分类获得其他类型。为了验证所提出的方法,比较了NN分类器和NN-SEaTH方法。所提出的技术被证明可以提高 4 年的整体准确度 (OA) 和 kappa 系数 (k)。OA 和 k 分别为 96.46% 和 0.9231、96.63% 和 0.9269、96.88% 和 0.9394、95.22% 和 0.9239,远大于 88.13% 和 0.7503、806.808。88.64% 和 0.7630、87.33% 和 0.7371 来自 NN 方法。该研究为通过分层分类的基于中等分辨率的土地覆盖制图提供了参考。
更新日期:2020-07-01
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