Asynchronous fusion is one of the barriers among multisensor data fusion, and asynchronous track fusion is an important research aspect of asynchronous fusion for its practicability in application. At present, local prediction weighted fusion method is used extensively in processing asynchronous track fusion. In the current algorithms, the global fusion estimate isn't obtained by local sensors; thereby the local estimate can't be improved by the global estimate. This is because that there is no feedback communication link between the fusion center and local sensors, accordingly, the performance of the track fusion system is reduced. In order to improve the estimate precision of the fusion system, the global feedback is introduced in this paper, and the corresponding asynchronous track fusion algorithm is presented. Compared with the current algorithm without feedback, the proposed algorithm can effectively improve the estimate precision not only in local sensors but also in the fusion center, and the proofs are given in the appendix. The simulations and algorithm analysis both show the advantages of the novel algorithm.
The existing remote sensing image datasets target the identification of objects, features, or man-made targets but lack the ability to provide the date and spatial information for the same feature in the time-series images. The spatial and temporal information is important for machine learning methods so that networks can be trained to support precision classification, particularly for agricultural applications of specific crops with distinct phenological growth stages. In this paper, we built a high-resolution unmanned aerial vehicle (UAV) image dataset for middle-season rice. We scheduled the UAV data acquisition in five villages of Hubei Province for three years, including 11 or 13 growing stages in each year that were accompanied by the annual agricultural surveying business. We investigated the accuracy of the vector maps for each field block and the precise information regarding the crops in the field by surveying each village and periodically arranging the UAV flight tasks on a weekly basis during the phenological stages. Subsequently, we developed a method to generate the samples automatically. Finally, we built a high-resolution UAV image dataset, including over 500,000 samples with the location and phenological growth stage information, and employed the imagery dataset in several machine learning algorithms for classification. We performed two exams to test our dataset. First, we used four classical deep learning networks for the fine classification of spatial and temporal information. Second, we used typical models to test the land cover on our dataset and compared this with the UCMerced Land Use Dataset and RSSCN7 Dataset. The results showed that the proposed image dataset supported typical deep learning networks in the classification task to identify the location and time of middle-season rice and achieved high accuracy with the public image dataset.
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