It is evident that using complementary features from different sensors is effective for land cover classification. Therefore, combining complementary information from hyperspectral (HS) and light detection and ranging (LiDAR) data can greatly assist in such applications. In this paper, we propose a model for land cover classification, which extracts effective features representing different characteristics (e.g., spectral, geometrical/structural) of objects of interest from these two complementary data sources (e.g., HS and LiDAR) and fuse them effectively by incorporating dimensionality reduction technique. The HS bands are first grouped based on their joint entropy and structural similarity for group-wise spatial feature extraction. The spectral and spatial features from HS are then fused in parallel via discriminant correlation analysis (DCA) method for each band group. This is followed by a multisource fusion step between the spatial features extracted from HS and LiDAR data using DCA. The resultant features from both band-group fusion and multisource fusion steps are concatenated with several other features extracted from HS and LiDAR data. In the proposed model, DCA fusion produces discriminative features by eliminating between-class correlations and confining within-class correlations. We compare the performance of our feature extraction and fusion scheme using random forest and support vector machine classifiers. We also compare our approach with several state-of-the-art approaches on two benchmark land cover datasets and show that our approach outperforms the alternatives by a large margin.