CAJ | 학술논문

为寻求一种有效的提高多源遥感数据土地覆被分类制图精度的方法，探讨了融合 HJ1B 和 ALOS ／PALSAR图像进行遥感图像分类制图的方法。在对光学图像 HJ1B 和雷达遥感数据 ALOS ／PALSAR 进行离散小波融合的基础上，应用分类决策树 CART（Classification and Regression Tree）算法对融合的图像进行了土地覆被分类制图，并将其分类结果与支持向量机 SVM（Support Vector Machine）分类结果进行对比。研究结果表明：将光学和雷达图像数据进行离散小波融合，采用分类决策树 CART 和支持向量机 SVM进行图像分类，CART 的分类精度要优于 SVM的结果。可见，在光学图像 HJ1B 和雷达数据 ALOS ／PALSAR 融合的基础上，应用 CART 能有效进行地物识别，提高图像的分类精度。
위심구일충유효적제고다원요감수거토지복피분류제도정도적방법，탐토료융합 HJ1B 화 ALOS ／PALSAR도상진행요감도상분류제도적방법。재대광학도상 HJ1B 화뢰체요감수거 ALOS ／PALSAR 진행리산소파융합적기출상，응용분류결책수 CART（Classification and Regression Tree）산법대융합적도상진행료토지복피분류제도，병장기분류결과여지지향량궤 SVM（Support Vector Machine）분류결과진행대비。연구결과표명：장광학화뢰체도상수거진행리산소파융합，채용분류결책수 CART 화지지향량궤 SVM진행도상분류，CART 적분류정도요우우 SVM적결과。가견，재광학도상 HJ1B 화뢰체수거 ALOS ／PALSAR 융합적기출상，응용 CART 능유효진행지물식별，제고도상적분류정도。
In order to increase the accuracy of the land use and land cover (LULC)classification via multi-source remote sensing data,we explored an effective algorithm by fusion of HJ1B images from optical sensors and ALOS /PALSAR data from radar remote sensing.In the process of fusion,the discrete wavelet transform (DWT)was uti-lized.The land-cover classification mapping was performed by using the classification and regression tree (CART) approach.The classification result by CRT approach was compared with that by support vector machine (SVM)ap-proach.The results show that:1)through fusing HJ1B optical images with ALOS /PALSAR radar data,we obtain an overall Kappa coefficient (0.826 9)and total accuracy(85.60 %)by CRT approach,while by SVM approach the value is 0.816 7 and 84.82 %,respectively;2)in terms of classification accuracy,CRT approach is superior to SVM approach;3)by means of fusing optical images with radar data ,we can effectively carry out object recogni-tion and improve classification accuracy through applying CART approach.