Agile Earth observation satellite scheduling is crucial for space-based remote-sensing services. The sharply rising demands and explosion of the solution space pose significant challenges to the optimization of observation task scheduling. To address this issue, we propose a deep reinforcement learning-based attention decision network (ADN) to determine the task scheduling sequence. We also construct a Markov decision process model in which the original and direct attributes are defined to describe the environment and used as the input of the ADN. Moreover, a start-time-shift-based local search is proposed to improve the observation plan generated by the ADN model. A comprehensive experiment was conducted, and the results proved that the attention mechanism in our ADN was beneficial for the training process to converge to better strategies. Compared with other advanced algorithms, the proposed method obtained a better total profit in the test sets. Furthermore, our methods exhibit considerable time efficiency, even for large-scale problems.
Monitoring built-up dynamics is essential for sustainable urban and territorial planning. This study presents an innovative geospatial methodology integrating multi-temporal satellite data fusion, transfer learning, machine learning classification, and open-access cloud computing…
The analysis and evaluation of landslide susceptibility are of great significance in preventing and managing geological hazards. Aiming at the problems of insufficient information caused by the limited number of landslide datasets, complex information of landslide evaluation fact…
Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies…
Sparse synthetic aperture radar (SAR) imaging has demonstrated excellent potential in image quality improvement and data compression. However, conventional observation matrix-based methods suffer from high computational overhead, which is hard to use for real data processing. The…
Leveraging mid-resolution satellite images such as Landsat 8 for accurate farmland segmentation and land change monitoring is crucial for agricultural management, yet is hindered by the scarcity of labelled data for the training of supervised deep learning pipelines. The particul…
Flooding is a recurrent hazard occurring worldwide, resulting in severe losses. The preparation of a flood susceptibility map is a non-structural approach to flood management before its occurrence. With recent advances in artificial intelligence, achieving a high-accuracy model f…