Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, trees, and shadows. The objective of this paper is to enhance context and strip features of the road by designing UNet-like architecture. The overall method first enhances the context characteristics in the segmentation step and then maintains the stripe characteristics in a refinement step. The segmentation step exploits an attention mechanism to enhance the context information between the adjacent layers. To obtain the strip features of the road, the refinement step introduces the strip pooling in a refinement network to restore the long distance dependent information of the road. Extensive comparative experiments demonstrate that the proposed method outperforms other methods, achieving an overall accuracy of 98.25% on the DeepGlobe dataset, and 97.68% on the Massachusetts dataset.
In the actual environment of security detection, many kinds of liquids often exist in the same detection background, and their dangerous levels are difficult to identify. Therefore, it is very important to research on identifying the dangerous levels of various liquids. The paper establishes the [Formula: see text]-parameter database of tested samples under specific detection environment with free space method. In the actual detection, ultra-wide-band (UWB) centimeter wave is used to measure the [Formula: see text]-parameters of several detected liquids first. Then the fast independent component analysis (FastICA) algorithm is used for unmixing the mixed signal by Newton’s iteration method and the negative entropy maximization search principle. The unmixed signal matches with the sample database adaptively, so the dangerous levels of the detected liquids are identified. Multiple experiments show that FastICA algorithm can reach a matching rate of 95% between water and 90[Formula: see text] gasoline or alcohol and 90[Formula: see text] gasoline, it also can reach a matching rate of around 73% between water and alcohol. This algorithm has a quick response and high reliability for identification of dangerous liquids. FastICA algorithm in this paper is applied for detecting the dangerous liquids for the first time, and it has high application value.
In order to observe dim astronomical objects, the imaging sensor of the Astronomical Observation Camera (AOC) in the satellite needs to work at -65 to suppress its dark current noise. To achieve the low temperature environment, a cooling system on Thermoelectric Cooler (TEC) was designed. The cooling system adopted a Single Chip Microprocessor as its control computing platform and its software used self-adaptive digital PID control algorithm was developed. The data of the real-time temperatures, captured by the acquiring circuits from the temperature sensors which were mounted at the back surface of the imaging sensor, were used to be the input of the selfadaptive digital PID control algorithm, and the output calculated by the algorithm was applied to adjusted the control current of the driver circuit for the TEC, and then the temperature of Focal Plane Assembly (FPA) was cooled down or warmed up to satisfy the working requirements. Experimental results show that the designed system can control the temperature gradient of the FPA less than 1 and reach its normal working temperature -65 quickly. Its control accuracy is ±2 . The practical experiment results displayed the imaging performance of the FPA was stable and the designed cooling system suppressed the dark current noise effectively to get high Signal-to-Noise Ratio images, and the designed cooling system worked stable.
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