AVHRR composite period selection for land cover classification
AVHRR composite period selection for land cover classification
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土地覆盖分类的 AVHRR 复合时期选择
DOI：
10.1080/01431160210145579
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
P. L. Chapman
中科院分区：
文献类型：
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作者：
S. Maxwell;R. M. Hoffer;P. L. Chapman
多时相卫星图像数据集提供了有关植被物候特征的宝贵信息，从而与单日期分类相比显着提高了覆盖类型分类的准确性。然而，在处理与多光谱数据相结合的多时相数据时，这些数据集的处理可能会变得非常复杂。高级甚高分辨率辐射计 (AVHRR) 双周复合数据通常用于对大区域的土地覆盖进行分类。可能需要选择这些双周复合周期的子集，以降低土地覆盖绘图的复杂性和成本。我们研究的目的是评估减少复合周期的数量和改变这些复合周期的间距对分类准确性的影响。由于年际变化会对分类结果产生重大影响，因此对 5 年的 AVHRR 数据进行了评估。覆盖整个生长季节的 AVHRR 双周光谱通道 1-4（可见光、近红外和两个热波段）合成图像用于对整个科罗拉多州五年不同年份的 14 种覆盖类型进行分类。采用监督分类方法来保持每个测试案例的程序一致。结果表明，复合周期的数量可以减半（从 14 个复合日期减少到 7 个复合日期），而不会显着降低总体分类准确性（14 个复合数据集的 Kappa 准确度为 80.4%，而 7 个复合数据集的 Kappa 准确度为 80.0%）。至少需要七个复合时期才能确保分类精度不受气候波动导致的年际变化的影响。与使用均匀间隔的时间段相比，在生长季节开始和结束时集中更多的复合材料，在测试的 5 年中始终产生略高的分类值（重度早/晚情况的平均 Kappa 为 80.3%，而备用数据集情况的平均 Kappa 为 79.0%）。
Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, nearinfrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite dataset as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).