CAJ | 학술논문

高分辨率遥感影像细节丰富，具有类内差异大、类间差异不明显的特点。为此，模拟人的目视解译方式，提出一种基于共享特征的多级二叉树分类算法，把多类分类问题划分为多个两类分类问题，每级两类分类都提取共享特征，仅解译一类目标，已解译的类别不再参加后面的分类，利用这样的逐步淘汰机制完成一幅遥感影像的全部解译。实验结果表明，与K近邻、支持向量机等其他多类分类算法相比，该算法具有更高的分类精度。
고분변솔요감영상세절봉부，구유류내차이대、류간차이불명현적특점。위차，모의인적목시해역방식，제출일충기우공향특정적다급이차수분류산법，파다류분류문제화분위다개량류분류문제，매급량류분류도제취공향특정，부해역일류목표，이해역적유별불재삼가후면적분류，이용저양적축보도태궤제완성일폭요감영상적전부해역。실험결과표명，여K근린、지지향량궤등기타다류분류산법상비，해산법구유경고적분류정도。
High resolution remote sensing images with abundant details generally have characteristics of great within class differences and unobvious between class differences. Simulating the visual interpretation, this paper proposes a multi-stage binary tree-structured classification algorithm based on sharing features. The multi-class classification problem is divided into multiple binary classification problems, sharing features are extracted to interpret objects of only one class at each binary classification stage, and each interpreted class will not participate in later classification. The proposed method makes use of the phase-out mechanism to complete the whole interpretation of a remote sensing image. Experimental results show that this algorithm has higher classification accuracy compared with other multi-class classification algorithms like K Nearest Neighbor(KNN), Support Vector Machine(SVM) and so on.