Gustavo Bayma Siqueira Silva, M. P. Mello, Y. Shimabukuro, B. Rudorff, Daniel de Castro Victoria
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引用次数: 5
Abstract
Spectral-Temporal Analysis by Response Surface (STARS), which utilizes surface response to represent time series spectral-temporal behavior of pixels in satellite images, was used to map and discriminate savanna vegetation classes in portion of Cerrado biome of Mato Grosso State, Brazil, using MODIS data. STARS utilized 16 daily MODIS, cloud-free, images that were collected from September 1st 2008 to August 31st 2009. The Multi-Coefficient Image (MCI) resulted from the STARS was used as input attributes for the three tested classifiers: (i) ML — maximum likelihood; (ii) SVM — support vector machine; and (iii) NN — neural network. The results showed that the NN classifier presented higher kappa coefficient (0.58) and overall accuracy of 68.6%.
响应面光谱-时间分析(STARS)利用地表响应来表示卫星图像中像素的时间序列光谱-时间行为，利用MODIS数据对巴西马托格罗索州塞拉多生物群部分地区的稀树草原植被分类进行了制图和区分。STARS利用了从2008年9月1日到2009年8月31日收集的16张每日MODIS无云图像。由STARS产生的多系数图像(MCI)被用作三个测试分类器的输入属性:(i) ML -最大似然;支持向量机-支持向量机;(iii) NN -神经网络。结果表明，该分类器具有较高的kappa系数(0.58)和68.6%的总体准确率。
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