In recent years, the satellite videos have been captured by a moving satellite platform. In contrast to consumer, movie, and common surveillance videos, satellite video can record the snapshot of the city-scale scene. In a broad field-of-view of satellite videos, each moving target would be very tiny and usually composed of several pixels in frames. Even worse, the noise signals also existed in the video frames, since the background of the video frame has the subpixel-level and uneven moving thanks to the motion of satellites. We argue that this is a new type of computer vision task since previous technologies are unable to detect such tiny vehicles efficiently. This paper proposes a novel framework that can identify the small moving vehicles in satellite videos. In particular, we offer a novel detecting algorithm based on the local noise modeling. We differentiate the potential vehicle targets from noise patterns by an exponential probability distribution. Subsequently, a multi-morphological-cue based discrimination strategy is designed to distinguish correct vehicle targets from a few existing noises further. Another significant contribution is to introduce a series of evaluation protocols to measure the performance of tiny moving vehicle detection systematically. We annotate a satellite video manually and use it to test our algorithms under different evaluation criterion. The proposed algorithm is also compared with the state-of-the-art baselines, and demonstrates the advantages of our framework over the benchmarks.
Nowadays many observation technologies have been developed and are of enormous significance, including optical satellite images, space-borne synthetic aperture radar (SAR) images and aerial videos. Such technology plays a critical role in both civil and military area, such as the city traffic system, maritime surveillance, aerial spy and battlefield monitor, . Both optical satellite image and space-borne SAR can observe a large area in a high resolution. However, optical satellite is very susceptible to different illumination and various weather. Although SAR has the unique capability of earth imaging in all-whether regardless of day and night @cite_27 , the SAR images are difficult to be interpreted @cite_0 @cite_6 . Another weakness in understanding the optical satellite image and SAR is that they cannot observe dynamics due to stationary imaging, which narrows down their applications.
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This paper proposes a novel method for ship detection and discrimination in complex background from synthetic aperture radar (SAR) images. It first implements a pixel-level land–sea segmentation with the aid of a global 250-m water mask. Then, an efficient multiscale constant false alarm rate (CFAR) detector with generalized Gamma distribution clutter model is designed to detect candidate targets in the sea. At last, eigenellipse discrimination and maximum-likelihood (ML) discrimination are designed to further exclude false alarm nonship objects in nearshore and harbor area. The proposed land–sea segmentation method is compared with multilevel Otsu method. The proposed multiscale ship detector is compared with conventional CFAR detectors. These contrast experiments show the good performance of our method. Finally, experiments undertaken on actual ALOS-2 SAR data show the efficacy of the proposed approach in detecting nearshore ship targets in a complex coastal environment. Rapid advances in synthetic aperture radar (SAR) technologies have brought challenges in image interpretation toward the development of new Earth observation applications. Both novel scattering–imaging models and intelligent inversion techniques are required for advanced information retrieval and interpretation of high-resolution multidimension and multimode SAR data. As an example, scene reconstruction attempts to transfer the SAR image to human-understandable representation of man-made targets and natural environment. In this letter, a framework for scene reconstruction is outlined. It includes three key elements: a dictionary of parametric scatterer model, a method for scatterer recognition and parameter estimation, and a method for target reconstruction. A preliminary case is presented, where a simulated 3-D SAR image of a simple target is successfully reconstructed to a solid geometry. It uses a novel surface extension method to connect isolated scatterers to form a complete target geometry.