An Improved Mapping with SuperResolved Multispectral Images for Geostationary Satellites Superresolution (SR) technology has shown great potential for improving the performance of the mapping and classification of multispectral satellite images. However, it is very challenging to solve illconditioned problems such as mapping for remote sensing images due to the presence of complicated ground features. In this paper, we address this problem by proposing a superresolution reconstruction (SRR) mapping method called the mixed sparse representation nonconvex highorder total variation (MSRNCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSRNCHOTV is employed to reconstruct highresolution images from lowresolution timeseries images obtained from the Gaofen4 (GF4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF4 geostationary orbit satellite images. Two sets of GF4 satellite image data were used for experiments, and the MSRNCHOTV SRR result obtained using these data was compared with the SRR results obtained using the bilinear interpolation (BI), projection onto convex sets (POCS), and iterative back projection (IBP) methods. The sharpness of the SRR results was evaluated using the graylevel variation between adjacent pixels, and the signaltonoise ratio (SNR) of the SRR results was evaluated by using the measurement of high spatial resolution remote sensing images. For example, compared with the values obtained using the BI method, the average sharpness and SNR of the five bands obtained using the MSRNCHOTV method were higher by 39.54% and 51.52%, respectively, and the overall accuracy (OA) and Kappa coefficient of the classification results obtained using the MSRNCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSRNCHOTV method can effectively improve image clarity, enrich image texture details, enhance image quality, and improve image classification accuracy. Thus, the effectiveness and feasibility of using the proposed SRR method to improve the classification accuracy of remote sensing images was verified.

Superresolution with Nonlocal Regularized Sparse Representation The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem. The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by introducing the l 1 norm sparsity prior into the superresolution reconstruction process. In this paper, we present a new SR based image superresolution by optimizing the objective function under an adaptive sparse domain and with the nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed method can reconstruct HR images with sharp edges from degraded LR images.