Deep learning based multi-label image annotation is receiving increasing attention in the field of remote sensing due to the great success of deep networks in single-label remote sensing image classification. Compared with those low-level features, the features extracted by the convolutional neural network (CNN) are more informative and can alleviate the problem of semantic gap. However, the CNN model tends to ignore the smaller objects when objects of different sizes exist in an image. In addition, how to efficiently leverage the correlation among multiple labels to enhance annotation performance remains an open issue. In this paper, we propose an end-to-end deep learning framework for multi-label remote sensing image annotation. The framework is composed of a multi-scale feature fusion module, a channelspatial attention learning module and a label correlation extraction module. The multi-scale features from different layers of a CNN model are first fused and refined by using a channel-spatial attention mechanism. Then, the label correlation information is extracted from a label co-occurrence matrix, and embedded into the multi-scale attentive features to increase the discriminative ability of the resulting image features. The experiments on two benchmark data sets demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.

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Thermal power units account for a large proportion of the total installed capacity of Shandong power grid, so the output adjustment of thermal power units has a great impact on Shandong power grid. This paper analyzes all the output adjustment of thermal power units in Shandong power grid in November 2019, and puts forward a classification method of output adjustment for units with different capacity levels, gives the verification method of output adjustment, and puts forward the suggestions for output adjustment.

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