高光谱与LiDAR数据融合研究——以黑河中游张掖绿洲农业区精细作物分类为例

杨思睿, 薛朝辉, 张玲, 苏红军, 周绍光

Fusion of hyperspectral and LiDAR data: A case study for refined crop classification in agricultural region of Zhangye Oasis in the middle reaches of Heihe River

Sirui YANG, Zhaohui XUE, Ling ZHANG, Hongjun SU, Shaoguang ZHOU

表2 基于2种形式的SMLR对多源特征组合(EMAP和EAP)分类的精度与Kappa系数

Tab.2 Classification accuracy and Kappa coefficient for multi features combination (EMAP,EAP) based on SMLR in two forms(%)

序号	类别	linear				RBF-MRF
序号	类别	X^h+ EMAP(X^h)	X^h+ EAP(X^L)	EAP(X^L)+ EMAP(X^h)	X^h+EAP(X^L)+ EMAP(X^h)	X^h+ EMAP(X^h)	X^h+ EAP(X^L)	EAP(X^L)+ EMAP(X^h)	X^h+EAP(X^L)+ EMAP(X^h)
1	玉米	91.71	89.31	87.97	90.91	91.79	90.31	90.99	91.27
2	韭菜	95.21	92.26	91.37	94.69	96.54	91.64	86.96	92.42
3	菜花	90.00	87.39	94.99	94.85	94.86	91.59	95.45	93.82
4	菜椒	93.36	89.64	89.32	93.20	96.17	92.59	96.64	94.76
5	土豆	95.43	95.21	87.09	93.94	96.89	94.92	95.38	97.56
6	冬笋	98.31	98.49	95.25	98.29	97.83	98.98	98.98	99.11
7	西瓜	86.05	93.51	94.64	92.22	97.08	92.18	95.40	97.38
8	建设用地	86.49	82.81	78.16	83.40	97.19	98.31	96.31	98.68
OA		90.36	87.72	85.98	89.53	94.53	93.33	93.85	94.50
AA		92.07	91.08	89.85	92.69	96.04	93.81	94.51	95.62
Kappa		86.65	83.17	80.80	85.58	92.39	90.72	91.45	92.35