多光谱遥感影像的光谱规则分类算法修正及海岛、海岸带应用

Modification of Spectral Rule-based Classifier based on multispectral remote sensing images and its application in islands and coastal zones

在光谱规则分类算法(Spectral Rule-based Classifier, SRC)基础上考虑大气校正对遥感影像光谱反射率的影响,提出了一种改进光谱规则的分类算法(Modified Spectral Rule-based Classifier, MSRC),从地物光谱响应曲线和光谱指数两个方面来修正光谱规则集,通过规则细化和补充、阈值改正优化光谱类别。以珠江三角洲海岛(佳蓬、淇澳)和海岸带(荃湾、惠东)的Landsat 8影像作为实验数据,对比了大气校正前后波段反射率和地物光谱响应曲线,分析了改进后MSRC算法的地物分类结果和精度,并与原SRC算法、最小距离分类(MDC)算法、最大似然分类(MLC)算法、支持向量机分类(SVM)算法、神经网络分类(NNC)算法以及基于光谱指数的算法等多种地物分类算法进行比较。结果表明,4组实验数据的MSRC算法分类结果总体精度分别为87.66%、82.38%、77.67%和80.05%,高于SRC、MDC、MLC和基于光谱指数的分类算法,在无需人工标注训练数据集的前提下接近SVM和NNC算法的分类精度。MSRC算法适用于海岛和海岸带的Landsat 8多光谱遥感影像。

Based on the unsupervised pixel-based Spectral Rule-based Classifier(SRC) algorithm, an effective Modified Spectral Rule-based Classifier(MSRC) was proposed considering the influence of atmospheric correction on spectral reflectances of remote sensing images. MSRC modifies rule sets according to ground object spectrum curves and spectral indices, optimizes spectral categories through refined and supplementary rules as well as modified thresholds. The Landsat 8 remote sensing images of islands(Jiapeng, Qi’ao) and coastal zones(Quanwan, Huidong) in the Pearl River Delta were chosen as the experimental data. The band reflectances and ground object spectrum curves before and after atmospheric correction process were contrasted. Classification results and accuracy of the MSRC algorithm were analyzed and compared with those of other six classification algorithms: the original SRC algorithm, Minimum Distance Classification(MDC) algorithm, Maximum Likelihood Classification(MLC) algorithm, Support Vector Machine(SVM) algorithm, Neural Network Classification(NNC) algorithm and spectral indices-based classification methods. The overall accuracy(OA) of MSRC algorithm using experimental data were respectively 87.66%, 82.38%, 77.67% and 80.05%, which were all higher than those of the original SRC algorithm, MDC, MLC and spectral indices-based classification methods, and closed to the accuracy of supervised algorithms(SVM and NNC) without the requirement of manually labelling the training dataset. MSRC performs well in land-cover type scenarios of islands and coastal zones on Landsat 8 multispectral remote sensing images.