Spatiotemporal fusion methods are considered a useful tool for generating multi-temporal reflectance data with limited high-resolution images and necessary low-resolution images. In particular, the superiority of sparse representation-based spatiotemporal reflectance fusion model (SPSTFM) in capturing phenology and type changes of land covers has been preliminarily demonstrated. Meanwhile, the dictionary training process, which is a key step in the sparse learning-based fusion algorithm, and its effect on fusion quality are still unclear. In this paper, an enhanced spatiotemporal fusion scheme based on the single-pair SPSTFM algorithm has been proposed through improving the process of dictionary learning, and then evaluated using two actual datasets, with one representing a rural area with phenology changes and the other representing an urban area with land cover type changes. The validated strategy for enhancing the dictionary learning process is divided into two modes to enlarge the training datasets with spatially and temporally extended samples. Compared to the original learning-based algorithm and other employed typical single-pair-based fusion models, experimental results from the proposed fusion method with two extension modes show improved performance in modeling reflectance using the two preceding datasets. Furthermore, the strategy with temporally extended training samples is more effective than the strategy with spatially extended training samples for the land cover area with phenology changes, whereas it is opposite for the land cover area with type changes.

Since requirements of related applications for time series remotely-sensed images with high spatial resolution have been hard to be satisfied under current observation conditions of satellite sensors, it is key to reconstruct high-resolution images at specified dates. As an effective data reconstruction technique, spatiotemporal fusion can be used to generate time series land surface parameters with a clear geophysical significance. In this study, an improved fusion model based on the Sparse Representation-Based Spatiotemporal Reflectance Fusion Model (SPSTFM) is developed and assessed with reflectance data from Gaofen-2 Multi-Spectral (GF-2 MS) and Gaofen-1 Wide-Field-View (GF-1 WFV). By introducing a spatially enhanced training method to dictionary training and sparse coding processes, the developed fusion framework is expected to promote the description of high-resolution and low-resolution overcomplete dictionaries. Assessment indices including Average Absolute Deviation (AAD), Root-Mean-Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), spectral angle mapper (SAM), structure similarity (SSIM) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS) are then used to test employed fusion methods for a parallel comparison. The experimental results show that more accurate prediction of GF-2 MS reflectance than that from the SPSTFM can be obtained and furthermore comparable with popular two-pair based reflectance fusion models like the Spatial and Temporal Adaptive Fusion Model (STARFM) and the Enhanced-STARFM (ESTARFM).

The comprehensive quality evaluation of distribution network equipment assets in the whole life cycle plays an important role in the development of distribution network enterprises. The quality evaluation of individual equipment is not representative, and it is not feasible to evaluate each piece of equipment. So, in this paper, the average quality level of similar distribution network equipment is evaluated from four aspects, procurement, installation, operation, and obsolescence, to make the evaluation results more representative and help distribution network enterprises to master the status of their equipment assets more efficiently. Under the evaluation index system, the analytic hierarchy process, the entropy weight method, and the least square method are used to get the comprehensive weight composed of the subjective weight and objective weight mixing. Then, by the fuzzy comprehensive evaluation method, the comprehensive weight is used to evaluate similar equipment in different regions and different equipment in the same region. Finally, the case analysis is carried out with the data of distribution cables and distribution transformers in H, Z, and J provinces to verify the feasibility and effectiveness of the proposed index evaluation system and evaluation model.

With the orderly advancement of power system reform and the rapid development of incremental power distribution pilots, the investment process of power grid companies has become more and more transparent and standardized. However, in the current fierce environment, how to maintain certain economic benefits in various regional distribution networks has become a problem of concern. This paper establishes a distribution network asset economic index system from the three dimensions of investment cost, operating cost, and economic benefit. The hierarchical structure of indicators is clarified based on the interpretative structural model, and the economic level is measured based on the improved analytical hierarchy process AHP-fuzzy evaluation model. Finally, the analysis of calculation examples shows that: (1) The investment cost and operating cost of the distribution network indirectly affect the economic benefits of the power grid by affecting the electricity sales. (2) The economic levels of the distribution network in the three regions of X, Y, and Z are good, medium, and poor. The region X needs to strengthen the management of distribution network investment, and the region Y continues to improve the economy from all aspects of cost and income. The first thing in region Z is to improve the level of economic efficiency.

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