With the improvement of hardware computing power, the application of deep learning methods in the field of remote sensing is increasing. This paper summarizes the progress of deep learning methods in remote sensing image object detection in recent years. The main methods of deep learning methods to extract and use target feature information in various target detection tasks are summarized. Finally, the application trend of deep learning methods in the field of remote sensing image detection is prospected.
Target detection of arbitrary shape is widely used in remote sensing image processing, the case segmentation method based on contour regression is very similar to the target detection method. At present, many scholars have studied case segmentation using deep learning framework, case segmentation based on contour regression can be regarded as a simple extension of object detection, it is a more accurate target detection. In this paper, the contour regression method based on Fourier operator is used for accurate target detection, the Fourier feature vectors in a group of frequencies are predicted at each position of the feature map, and then the target contour point sequence is reconstructed in the image space domain through inverse Fourier transform, The simulation results show that the detection accuracy of the proposed method is more than 10% higher than that of the classical method.
The theoretical model of atmospheric refractive index based on the standard atmospheric environment cannot explain the atmospheric refractive index when optical satellites detect targets. Aiming at this problem, a method for estimating the refractive index of the atmosphere based on multispectral stellar observation data is proposed. Based on optical satellites’ multispectral stellar observation data, according to the principle of deflection when the stellar light passes through the atmosphere, the optical path model of the stellar light refracted by the atmosphere is established under the assumption of a layered spherical atmosphere. A method of using multispectral segment stars to measure the actual light and the iterative forward feedback of stellar theoretical light is proposed, to estimate the refractive index of each layer of the atmosphere of the layered atmosphere to different spectral segments of light.
A problem of state estimation with a new constraints named incomplete nonlinear constraint is considered. The targets are often move in the curve road, if the width of road is neglected, the road can be considered as the constraint, and the position of sensors, e.g., radar, is known in advance, this info can be used to enhance the performance of the tracking filter. The problem of how to incorporate the priori knowledge is considered. In this paper, a second-order sate constraint is considered. A fitting algorithm of ellipse is adopted to incorporate the priori knowledge by estimating the radius of the trajectory. The fitting problem is transformed to the nonlinear estimation problem. The estimated ellipse function is used to approximate the nonlinear constraint. Then, the typical nonlinear constraint methods proposed in recent works can be used to constrain the target state. Monte-Carlo simulation results are presented to illustrate the effectiveness proposed method in state estimation with incomplete constraint.
Most multisensor association algorithms based on fuzzy set theory forms the opinion of fuzzy proposition using a simple triangular function. It does not take the randomness of measurements into account. Otherwise, the variance of sensors supposed to be known in the triangular function, but in fact the exact variance is difficult to acquire. This paper discuss about two situations with known and unknown variance of sensors. First, with known variance and known mean. This paper proposes a method, which use the probability ratio to calculate the fuzzy support degree. The interaction between the two objects is considered. Second, with unknown variance and known mean value, we replace the sample mean in the gray auto correlation function with the real sensor mean value to analysis the uncertainty which is the correlation coefficient between targets and measurements actually. In this way, it can deal with the case of small sample. Finally, form the opinion about the fuzzy proposition in terms of weighting the opinion of all the sensors based on the result of uncertainty analysis. Sufficient simulations on some typical scenarios are performed, and the results indicate that the method presented is efficient.
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