Optical remote sensing is widely used in relief and military affairs. However, its detection ability is limited by the contrast between target and background. Polarization imaging detection is different from traditional intensity detection methods. It can effectively detect and identify polarization pattern obvious targets with low contrast, but it has some shortcomings such as large system volume, complex system design and low light efficiency. Therefore, a polarization detection method based on dynamic vision sensor (DVS) is proposed in the paper. The feasibility of the method is studied and analyzed here. A simple experimental system based on DVS and rotating polarizer is built. Moreover, both indoor and outdoor experiments are carried out separately. The results show that our method can effectively detect the targets with different degrees of polarization (Dop) in the scene, and has the advantages of high sensitivity, intuitive detection and small physical size. It holds the potential applications in the field of remote sensing based man-made targets detection.
KEYWORDS: Particle filters, Target detection, Detection and tracking algorithms, Signal to noise ratio, Electronic filtering, Image processing, Surveillance systems
Particle filtering is a key technique for moving targets detection and tracking in the field of remote surveillance system and air defense systems. Moving targets can be tracked by particle filter without registration. However, standard particle filtering cannot suite for high-precision tracking and track small dim moving targets occupying a few pixels in image, having low signal-to-noise ratio (SNR) and always flicking. To solve this problem, an improved algorithm is proposed to achieve detection and tracking for small dim moving targets. In the new algorithm, the prediction process of particle filter is improved by a linear regression method. It is applicable to the sequential images where the moving targets become smaller and dimmer gradually. Small dim targets can be detected and tracked directly with low SNR and without registration. The trajectory of the moving target is learned automatically through the past state of the moving target, and the trajectory is used for generating the importance density function. The importance density function is used as the prior probability in particle filter to sample and update particles. Through continuously learning and updating the trajectory of the moving target, the tracking accuracy is improved. Experimental results show that the tracking accuracy of the moving targets is greatly improved, and small dim moving targets can be detected and tracked without registration.
High-resolution (HR) remote sensing images are characterized by rich and detailed ground object information with more complex structures of the ground object which make the interference information is more difficult to process. It has always been the focus of domestic and foreign researchers that how to obtain more accurate and higher quality ground object information from these images. The GF-4, the world's first geostationary orbit with high spatial resolution remote sensing satellite, can provide high temporal resolution, large width and 50m pixel resolution of remote sensing data by using area array imaging technology. However, the GF-4 image is a medium resolution and low resolution (LR) image data with relatively vague details of ground objects and not obvious relationships between objects which limit the acquisition of the ground object information to some extent. Therefore, in this paper, we analyze the influence of various factors in the imaging process and construct an image degradation model according to the characteristics of GF-4 satellite images. We adopted the super resolved (SR) method based on Mixed sparse representations (MSR) to increase the spatial resolution of the GF-4 image by twice as much, which not only enriched the detailed information of the image, but also improved the image quality. For the results of SR of GF-4 imagery, we adopted the Maximum Likelihood Classification (MLC) method to perform image classification test and result verification. The experimental area selected in this paper is Yantai City, Shandong Province, China, the LANDSAT 8 OLI data is used as a training sample to calculate the overall accuracy and Kappa coefficient after classification. The results show that the overall accuracy of the superreconstructed result data is 40% higher than that of the source image data from GF-4, especially when the spectral characteristics of the ground objects are obviously different, the accuracy is more obvious. The Kappa coefficient increased 0.4, the extracted outline is more complete and the classification details are more refined.
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