Improvement of Wheat Growth Information by Fusing UAV Visible and Thermal Infrared Images

To improve and enrich the wheat growth information, visible and thermal infrared (TIR) remote sensing images were simultaneously acquired by an unmanned aerial vehicle (UAV). A novel algorithm was proposed for fusing the visible and TIR images combing the intensity hue saturation (IHS) transform and regional variance matching (RVM). After registering the two images, IHS transform was first conducted to derive the Intensities of two images. Wavelet transform was then applied to the Intensities for obtaining the coefficients of low- and high-frequency sub-bands. The fusion rules of the fused image were developed based on regional correlation of wavelet decomposition coefficients. More specifically, the coefficients of low-frequency sub-bands were calculated by averaging the coefficients of two images. Regional variance was used to generate the coefficients of high-frequency sub-bands using the weighted template of a 3 × 3 pixel window. The inverse wavelet transform was used to create the new Intensity for the fused image using the low- and high-frequency coefficients. Finally, the inverse IHS transform consisting of the new Intensity, the Hue of visible image, and the Saturation of TIR image was adopted to change the IHS space to red–green–blue (RGB) color space. The fusion effects were validated by the visible and TIR images of winter wheat at the jointing stage and the middle and late grain-filling stage. Meanwhile, IHS and RV were also comparatively evaluated for validating our proposed method. The proposed algorithm can fully consider the correlation of wavelet coefficients in local regions. It overcomes the shortcomings (e.g., block phenomenon, color distortion) of traditional image fusion methods to obtain smooth, detailed and high-resolution images