Abstract
Super-resolution (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 ill-conditioned 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 super-resolution reconstruction (SRR) mapping method called the mixed sparse representation non-convex high-order total variation (MSR-NCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSR-NCHOTV is employed to reconstruct high-resolution images from low-resolution time-series images obtained from the Gaofen-4 (GF-4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF-4 geostationary orbit satellite images. Two sets of GF-4 satellite image data were used for experiments, and the MSR-NCHOTV 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 gray-level variation between adjacent pixels, and the signal-to-noise 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 MSR-NCHOTV 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 MSR-NCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSR-NCHOTV 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.
Highlights
Geostationary orbit remote sensing satellites have the advantages of high temporal resolution and large imaging width, which enables them to continuously cover large areas
The results show that the producer’s accuracy (PA) and user’s accuracy (UA) of the MSR-NCHOTV super-resolution reconstruction (SRR) image are significantly higher than those of the SRR images obtained using the three other methods, which proves that the MSR-NCHOTV method obtains a higher classification accuracy than these other methods
Compared with the SRR results obtained using the bilinear interpolation (BI) method [24], the sharpness and signal-to-noise ratio (SNR) of the five bands of the GF-4 satellite images obtained using the MSR-NCHOTV SRR method were higher by an average of 39.54% and 51.52%, respectively
Summary
Geostationary orbit remote sensing satellites have the advantages of high temporal resolution and large imaging width, which enables them to continuously cover large areas. Images from geostationary orbit satellites are frequently utilized in meteorology, environmental protection, fire monitoring, and other remote sensing applications [1,2]. The application of such images is becoming increasingly popular due to continuous worldwide efforts in developing a new generation of geostationary satellite sensors. Images from geostationary orbit remote sensing satellites offer an opportunity to use super-resolution reconstruction (SRR) technology to further improve image spatial resolution and mapping
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