基于随机森林和RankSVM优化的行人识别方法

本文针对卡口环境及大样本情况下,基于样本数据量大时对测试图像使用RankSVM排名结果会很靠后,提出了一种新的基于随机森林和RankSVM的行人识别方法RF-SVM(RondomForest SVM)。首先,单个训练样本提取多维特征向量,经K-means算法将所有训练样本的特征向量聚类,根据随机森林得到测试目标的预测类别,在此类范围内采用RankSVM算法,将相似度排名顺序作为行人识别结果。与传统方法相比,本文引用了随机森林预测分类的方法,避免了测试图像与全体样本进行相似度匹配,仅在预测到的类别中使用RankSVM,这样得到的排名结果既准确又相对单一的RankSVM排名结果更靠前,聚类算法结合随机森林起到一个对样本数据初筛的作用。本文基于VIPeR样本库的实验证明,该方法对行人姿态变化具有鲁棒性,相比MCC[10]与RankSVM等文中实验列举的传统算法识别准确率高。     

Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)

Invasive nonindigenous plants are threatening the biological integrity of North American rangelands, as well as the economies that are supported by those ecosystems. Spatial information is critical to fulfilling invasive plant management strategies. Traditional invasive plant mapping has utilized ground-based hand or GPS mapping. The shortfalls of ground-based methods include the limited spatial extent covered and the associated time and cost. Mapping vegetation with remote sensing covers large spatial areas and maps can be updated at an interval determined by management needs. The objective of the study was to map leafy spurge (Euphorbia esula L.) and spotted knapweed (Centaurea maculosa Lam.) using 128-band hyperspectral (5-m and 3-m resolution) imagery and assess the accuracy of the resulting maps. Beiman Cutler classifications (BCC) were used to classify the imagery using the randomForest package in the R statistical program. BCC builds multiple classification trees by repeatedly taking random subsets of the observational data and using random subsets of the spectral bands to determine each split in the classification trees. The resulting classification trees vote on the correct classification. Overall accuracy was 84% for the spotted knapweed classification, with class accuracies ranging from 60% to 93%; overall accuracy was 86% for the leafy spurge classification, with class accuracies ranging from 66% to 93%. Our results indicate that (1) BCC can achieve substantial improvements in accuracy over single classification trees with these data and (2) it might be unnecessary to have separate accuracy assessment data when using BCC, as the algorithm provides a reliable internal estimate of accuracy.