多分辨率低秩导向滤波的热红外图像空间融合

Owing to the special imaging principle in the long-wave infrared region, thermal infrared remote sensing images contain the temperature and emissivity features of targets. However, the low spatial resolution of thermal infrared images limits its wide application. With the development of remote sensing technology, multisource remote sensing images in the same region can provide complete information of a target for researchers. On the basis of the high spatial resolution of visible band images, thermal infrared image fusion enhancement and subpixel feature extraction have a high application value.Therefore, a new method named subpixel temperature retrieval of thermal infrared images based on multiresolution superpixel low-rank representation and residual correlation is proposed in this paper. The method achieves two goals by fusing visible and thermal infrared images in a super-resolution way: (1) enhancement of spatial characteristics for thermal infrared images based on adaptive fusion and (2) estimation of subpixel temperature and super resolution for thermal infrared images.The main processing and advantages of the algorithm are listed as follows: (1) For superpixel segmentation and low-rank restoration at multiresolution, superpixel blocks, instead of traditional blocks, are used as low-rank restoration units to enhance the stability of species in each unit and suppress structural noise. (2) Through constructing a guided linear filter, the high-spatial-resolution feature of the visible image can be transferred to the thermal infrared image while keeping the spectral information of the thermal infrared image unchanged. (3) For the estimation of subpixel temperature and super resolution of thermal infrared images, the correlation between the residuals of VIS and fusion images is established at the low-resolution layer and applied to the high-resolution layer to preserve image details.To validate the effectiveness of the proposed method, the visible and thermal infrared data in the 2014 IGARSS data fusion contest are used for experiments. The algorithm is evaluated in three aspects: (1) the improvement of spatial characteristics for thermal infrared images through adaptive low-rank representation, such as noise suppression, intraclass smoothing, and edge enhancement; (2) the spectral information protection of thermal infrared images in homogeneous and heterogenous regions; (3) the super-resolution effect and the accuracy of subpixel temperature retrieval. Compared with the traditional supervised graph-based feature fusion method, the proposed method has the best edge-sharpening, noise suppression, and spatial smoothing effects. It can protect the spectral information of thermal infrared images for different region types. The super-resolution image obtained by the proposed algorithm achieves high-temperature retrieval accuracy, and the overall root-mean-square error is less than 1 K. The average classification accuracy is improved by more than 20%.