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首页> 外文期刊>Photogrammetric Engineering & Remote Sensing: Journal of the American Society of Photogrammetry >Land Cover Classification and Feature Extraction from National Agriculture Imagery Program (NAIP) Orthoimagery: A Review
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Land Cover Classification and Feature Extraction from National Agriculture Imagery Program (NAIP) Orthoimagery: A Review

机译：土地覆盖分类和专题提取来自国家农业图像（Naip）OrthoImagery：综述

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摘要

This review describes the National Agriculture Imagery Program (NAIP), explores strengths and weaknesses of the data, and summarizes how the data are used in land cover and feature extraction tasks in order to provide some recommendations for future research and best practices for working with NAIP data. NAIP orthoimagery is an often-overlooked source for remote sensing classification and feature extraction applications in the contiguous United States (CONUS). NAIP data are free, or nearly free; are in the public domain; are available for all of the CONUS; comprise a multitemporal data set that spans more than a decade; and are collected at a high spatial resolution with generally very low cloud coverage. However, there are challenges associated with the use of these data. The low spectral resolution limits spectral differentiation, while the high spatial resolution results in very large data sets for study areas even as small as a single county. Differences in acquisition dates and time of day result in varying illumination conditions and potentially even varying phenological state. As a consequence, image digital number (DN) values can vary between adjacent tiles, and shadow size and direction can be inconsistent between different tiles and different acquisitions. Therefore, taking full advantage of this valuable data source requires the analyst to be cognizant of such concerns and take measures to deal with such inconsistency and minimize its impact on the classification results; future research addressing these concerns would further enhance the value of NAIP data.
机译：该审查描述了国家农业图像（Naip），探讨了数据的优势和弱点，并总结了如何在陆地覆盖中使用的数据和特征提取任务，以便为未来的研究和使用Naip工作的最佳实践提供一些建议数据。 Naip OrthoImagery是一种经常被忽视的遥感分类来源，在连续的美国（康斯）中的遥感分类和特征提取应用。 Naip数据是免费的，也是近自由的;在公共领域;适用于所有圆锥;包括跨越十多年来的多型数据集;并以高空间分辨率收集，具有非常低的云覆盖率。但是，与使用这些数据有关的挑战。低频分辨率限制了光谱分析，而高空间分辨率导致研究区域的非常大的数据集，甚至可以像单个县一样小。收购日期和日期的差异导致不同的照明条件和潜在的甚至不同的鉴生状态。结果，图像数字数（DN）值可以在相邻的图块之间变化，并且阴影大小和方向可以在不同的瓦片和不同的采集之间不一致。因此，充分利用这一有价值的数据源要求分析师认识到这些问题，并采取措施处理此类不一致，并尽量减少对分类结果的影响;解决这些问题的未来研究将进一步提高NAIP数据的价值。

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