Multisensor image fusion has gained tremendous significance due to various satellites operating in different parts of the electromagnetic spectrum. We present a hybrid fusion approach to integrate information from synthetic aperture radar (SAR) and multispectral (MS) imagery to improve land use land cover (LULC) classification. The major concern in SAR and optical fusion is the spectral distortion in the fused image, which is significantly less in pansharpening algorithms. The primary objective of our work is to inject unique spatial information from the SAR image into MS images, deriving enhanced data. The proposed approach is based on the integration of principal component analysis and wavelet decomposition to reduce spectral distortion in the fused image. Fused images are evaluated visually and statistically. Results are compared with conventional fusion approaches. In order to explore the effectiveness of the proposed technique, LULC classification is performed on the fused and original data. The LULC classification results are analytically compared with the standard thematic map to derive classification accuracy. A comparative analysis with other approaches conclusively proves that the proposed hybrid approach is superior to conventional approaches.