用随机决策树群算法进行高光谱遥感影像分类

Hyperspectral Remote Sensing Image Classification with Extremely Randomized Clustering Forests

高光谱影像具有丰富的光谱信息,与全色、多光谱影像相比能更好地进行地面目标的分类识别.该文对决策树分类算法的优劣进行分析,引入随机决策树群算法,对青海省祁连县Hyperion高光谱影像和IRS-P6影像数据进行实验,使用子空间划分和光谱距离进行降维后,分别采用支持向量机、神经网络、最大似然法进行分类,并与随机决策树群算法分类结果进行比较.结果表明,该算法表现最优且无需降维预处理,可广泛应用于高光谱遥感领域.

Hyperspectral images contain rich spectral information and have better performance in ground target recognition than panchromatic and multispectral images.An extremely randomized clustering forests （ERC-Forests） algorithm is introduced after analysis of the decision tree algorithm.Hyperion hyperspectral images and IRS-p6 image data of Qilian County,Qinghai Province,are used in the experiment.After dimension reduction with subspace methods and based on the spectral range,support vector machine（SVM）,neural network（NN） and maximum likelihood（MLC） are used for classification.The results are compared with that of random decision trees algorithm,showing that the extremely randomized clustering forests algorithm is better,without dimension reduction.The method is widely applicable to hyperspectral remote sensing.