OnOrbit SignaltoNoise Ratio Test Method for NightLight Camera in Luojia 101 Satellite Based on TimeSequence Imagery Nightlight remote sensing imaging technologies have increasingly attracted attention with the development and application of focal plane arrays. Onorbit signaltonoise ratio (SNR) test is an important link to evaluate nightlight cameraxe2x80x99s radiometric performance and the premise for quantitative application of remote sensing imageries. Under nightlight illumination conditions, the illuminance of ground objects is very low and varies dramatically, the spatial uniformity of each pixelxe2x80x99s output cannot be guaranteed, and thus the traditional onorbit test methods represented by variance method are unsuitable for lowresolution nightlight cameras. To solve this problem, we proposed an effective onorbit SNR test method based on consecutive timesequence images that including the same objects. We analyzed the radiative transfer process between nightlight camera and objects, and established a theoretical SNR model based on analysis of the generation and main sources of signal electrons and noise electrons. Finally, we took Luojia 101 satellite, the worldxe2x80x99s first professional nightlight remote sensing satellite, as reference and calculated the theoretical SNR and actual onorbit SNR using consecutive images captured by Luojia 101 satellite. The actual results show the similar characteristics as theoretical results, and are higher than the theoretical results within the reasonable error tolerance, which fully guarantee the detection ability of nightlight camera and verify the validity of this timesequencebased method.

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.