A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research
A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research
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DOI：
10.1016/j.rse.2016.02.028
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发表时间：
2016-05-01
影响因子：
13.5
通讯作者：
Stehman, Stephen V.
中科院分区：
文献类型：
--
作者：
Khatami, Reza;Mountrakis, Giorgos;Stehman, Stephen V.
遥感影像分类用于土地覆盖制图已经引起了研究者和实践者的极大关注。几十年来进行的大量研究调查了大量的输入数据和分类方法。然而，这些大量的研究结果还没有被综合起来，以提供关于产生土地覆盖产品的不同分类过程的相对性能的连贯指导。为了解决这一问题，我们对过去15年发表在5种高影响力遥感期刊上的有监督的每像素图像分类研究进行了统计荟萃分析。评估的两个一般因素是分类算法和输入数据操作，因为这些因素可以由分析人员控制以提高分类准确性。meta分析显示，加入纹理信息后，土地覆盖分类的整体精度提高幅度最大，平均提高12.1%。这种准确性的提高可以归因于包含纹理提供的额外空间上下文信息。纳入辅助数据、多角度图像和时间图像也显著提高了分类总体精度，分别提高了8.5%、8.0%和6.9%。相比之下，对光谱信息的其他操作，如指数创建（如归一化差异植被指数）和特征提取（如主成分分析）在准确性方面的改进要小得多。在分类算法方面，支持向量机的准确率最高，其次是神经网络方法。随机森林分类器的性能明显优于传统的决策树分类器。通常用作基准测试算法的最大似然分类器提供了较低的准确性。我们的研究结果将有助于指导从业者决定采用哪种分类，并为研究人员提供关于比较研究的方向，这将进一步巩固我们对不同分类过程的理解。然而，这些一般指导方针并不排除分析师将个人偏好或考虑可能与特定应用程序相关的特定算法利益。(C) 2016 Elsevier Inc.版权所有。
Classification of remotely sensed imagery for land-cover mapping purposes has attracted significant attention from researchers and practitioners. Numerous studies conducted over several decades have investigated a broad array of input data and classification methods. However, this vast assemblage of research results has not been synthesized to provide coherent guidance on the relative performance of different classification processes for generating land cover products. To address this problem, we completed a statistical meta-analysis of the past 15 years of research on supervised per-pixel image classification published in five high-impact remote sensing journals. The two general factors evaluated were classification algorithms and input data manipulation as these are factors that can be controlled by analysts to improve classification accuracy. The meta-analysis revealed that inclusion of texture information yielded the greatest improvement in overall accuracy of land-cover classification with an average increase of 12.1%. This increase in accuracy can be attributed to the additional spatial context information provided by including texture. Inclusion of ancillary data, multi-angle and time images also provided significant improvement in classification overall accuracy, with 8.5%, 8.0%, and 6.9% of average improvements, respectively. In contrast, other manipulation of spectral information such as index creation (e.g. Normalized Difference Vegetation Index) and feature extraction (e.g. Principal Components Analysis) offered much smaller improvements in accuracy. In terms of classification algorithms, support vector machines achieved the greatest accuracy, followed by neural network methods. The random forest classifier performed considerably better than the traditional decision tree classifier. Maximum likelihood classifiers, often used as benchmarking algorithms, offered low accuracy. Our findings will help guide practitioners to decide which classification to implement and also provide direction to researchers regarding comparative studies that will further solidify our understanding of different classification processes. However, these general guidelines do not preclude an analyst from incorporating personal preferences or considering specific algorithmic benefits that may be pertinent to a particular application. (C) 2016 Elsevier Inc. All rights reserved.