In this letter, the application of super-resolution (SR) techniques to GaoFen(GF)-4, which is the most advanced geostationary-orbit earth observing satellite in China, remote sensing images is investigated and tested. One of the shortcomings of the geostationary-orbit-based earth observing satellite is the limitation of spatial resolution. However, human beings never stop pursuing higher resolution in images. This is the first experiment of applying SR to a sequence of low-resolution (LR) images captured by GF-4 within a short time period. One of the barriers for applying SR to remote sensing images is the large time gaps between those LR image acquisition, because the reflection characteristic of the ground may change within the time period when those LR images were captured. However, GF-4 has the unique advantage of capturing a sequence of LR images of the same region in minutes, i.e., working as a staring camera from the point view of SR. The reconstructed high-resolution images of some regions in Beijing and Hainan are shown and evaluated in this letter. This letter demonstrates that the application of SR to geostationary-orbit-based earth observation data is feasible and valuable, and it has the potential to be applied to the images acquired by all other geostationary-orbit-based earth observing systems.

In this paper, a new framework of mixed sparse representations (MSRs) is proposed for solving ill-conditioned problems with remote sensing images. In general, it is very difficult to find a common sparse representation for remote sensing images because of complicated ground features. Here we regard a remote sensing image as a combination of subimage of smooth, edges, and point-like components, respectively. Since each domain transformation method is capable of representing only a particular kind of ground object or texture, a group of domain transformations are used to sparsely represent each subimage. To demonstrate the effect of the framework of MSR for remote sensing images, MSR is regarded as a prior for maximum a posteriori when solving ill-conditioned problems such as classification and super resolution (SR), respectively. The experimental results show that not only the new framework of MSR can improve classification accuracy but also it can construct a much better high-resolution image than other common SR methods. The proposed framework MSR is a competitive candidate for solving other remote sensing images-related ill-conditioned problems.

Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.

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.

Dictionary learning (DL) has been successfully applied to blind hyperspectral unmixing due to the similarity of underlying mathematical models. Both of them are linear mixture models and quite often sparsity and nonnegativity are incorporated. However, the mainstream sparse DL algorithms are crippled by the difficulty in prespecifying suitable sparsity. To solve this problem, this paper proposes an efficient algorithm to find all paths of the ℓ ₁ -regularization problem and select the best set of variables for the final abundances estimation. Based on the proposed algorithm, a DL framework is designed for hyperspectral unmixing. Our experimental results indicate that our method performs much better than conventional methods in terms of DL and hyperspectral data reconstruction. More importantly, it alleviates the difficulty of prescribing the sparsity.

Due to the fact that traditional High Dynamic Range (HDR) imaging methods cannot be used for satellites, finding a way to generate HDR remot e sensing images from satellites has long been explored in the field of remote sensing imaging. In this paper, a systematic method of synthesizing HDR remote sensing images based on a new registration algorithm and virtual digital delay integration (TDI) technology is proposed. Firstly, a series of original images are generated by the fast continuous shooting method through push-broom spaceborne camera. Then, a new registration algorithm with high accuracy and high robustness is proposed in this paper is used for image registration. Finally, an HDR multi-frame image synthesis algorithm is used to generate high-quality and high signal-to-noise-ratio HDR images. This technology greatly improves the image information acquisition capabilities of digital TDI area scan cameras