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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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. Inaccuracies in the observation model of the synthetic aperture radar (SAR) due to inaccuracies of the velocity and position of the platform or atmospheric turbulence cause degradations in reconstructed images which necessitate the use of autofocus algorithms. In this paper we propose a novel signal processing algorithm for joint SAR image formation and autofocus in a synthesis dictionary based sparse representation framework. Proposed algorithm can be applied broadly to scenes that exhibit sparsity with respect to any dictionary. This is done by extending our previously developed sparse representation-based SAR imaging framework to joint SAR image formation and autofocus. To this end, the phase error vector is separated from the unknown phase of the complex-valued back-scattered field. Phase error vector is estimated using a MAP estimator and compensated through an iterative algorithm to produce focused images. We demonstrate the effectiveness of the proposed approach on synthetic and real imagery. High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is an essential tool for modern remote sensing. To effectively deal with the contradiction problem between high-resolution and low pulse repetition frequency and obtain an HRWS SAR image, a multi-channel in azimuth SAR system has been adopted in the literature. However, the performance of the Doppler ambiguity suppression via digital beam forming processing suffers the losses from the channel mismatch. In this paper, a robust channel-calibration algorithm based on weighted minimum entropy is proposed for the multi-channel in azimuth HRWS SAR imaging. The proposed algorithm is implemented by a two-step process. 1) The timing uncertainty in each channel and most of the range-invariant channel mismatches in amplitude and phase have been corrected in the pre-processing of the coarse-compensation. 2) After the pre-processing, there is only residual range-dependent channel mismatch in phase. Then, the retrieval of the range-dependent channel mismatch in phase is achieved by a local maximum-likelihood weighted minimum entropy algorithm. The simulated multi-channel in azimuth HRWS SAR data experiment is adopted to evaluate the performance of the proposed algorithm. Then, some real measured airborne multi-channel in azimuth HRWS Scan-SAR data is used to demonstrate the effectiveness of the proposed approach. Airborne high resolution oblique imagery systems and RPAS UAVs are very promising technologies that will keep on influencing the development of geomatics in the future years closing the gap between terrestrial and classical aerial acquisitions. These two platforms are also a promising solution for National Mapping and Cartographic Agencies (NMCA) as they allow deriving complementary mapping information. Although the interest for the registration and integration of aerial and terrestrial data is constantly increasing, only limited work has been truly performed on this topic. Several investigations still need to be undertaken concerning algorithms ability for automatic co-registration, accurate point cloud generation and feature extraction from multiplatform image data. One of the biggest obstacles is the non-availability of reliable and free datasets to test and compare new algorithms and procedures. The Scientific Initiative “ISPRS benchmark for multi-platform photogrammetry”, run in collaboration with EuroSDR, aims at collecting and sharing state-of-the-art multi-sensor data (oblique airborne, UAV-based and terrestrial images) over an urban area. These datasets are used to assess different algorithms and methodologies for image orientation and dense matching. As ground truth, Terrestrial Laser Scanning (TLS), Aerial Laser Scanning (ALS) as well as topographic networks and GNSS points were acquired to compare 3D coordinates on check points (CPs) and evaluate cross sections and residuals on generated point cloud surfaces. In this paper, the acquired data, the pre-processing steps, the evaluation procedures as well as some preliminary results achieved with commercial software will be presented.