Moving target tracking from high resolution satellite videos has tremendous potential applications such as visual surveillance, traffic monitoring and so on. This paper proposed a moving target detection algorithm based on the optical flow of edge points for the video sequences in dynamic scene without image registration. Given two consecutive frames, they are firstly processed following with Canny edge operator. And then, the displacements of each pair of edges are computed based on bidirectional optical flow method, which provides high speed and precision. Thirdly, the displacement histogram of all the matched edges is established, which is used to eliminate the influence of the background for the images without registration. Finally, the edge points of moving target are matched based on edge constraint, and the moving target region is determined. Experiment results show that our method has an excellent performance of target tracking for the high resolution satellite videos without image registration.
Dense stereo matching has been widely used in lunar rover and it is still a challenging problem, and the main task is to calculate the disparity map given two rectified images of one scene. Most algorithms assume that a maximal possible disparity exists and search all disparities in the range from the minimum to this maximal disparity. In the case of large images and wide disparity search range this can be very computational cost. To solve these problems, we propose a novel hierarchical stereo matching that reconstructs the disparity map of the scene based on the pyramid image and more global matching (MGM) method. This strategy first generates an image pyramid from the original images. And then for the coarsest level images of the pyramid, the disparity map is computed based on the full disparity search range of the coarsest level images. The disparity map of the coarse image is then used as prior to restrict the disparity search space for finer layer matching. We conduct a number of experiments with lunar rover images to evaluate the performance of method, and the experimental results proved the total amount of calculation of the novel MGM method is only 10% of the previous method. And the speed of stereo matching is highly increased and is also more accurate on lunar scenes from the obtained dense disparity maps.
The effects of the ionosphere on spaceborne synthetic aperture radar (SAR) systems have received attention since the development of ALOS PALSAR (L-band). One of them is the Faraday rotation (FR) due to the dispersive nature of ionosphere and the existence of Earth’s magnetic field. The FR error is obviously embedded in polarimetric data of PALSAR systems, which destroys the scattering matrix. Nevertheless, distorted echoes contain abundant ionospheric information, the ionospheric sounding based on the scattering matrix data can become possible if the mechanisms of ionospheric interference can be understood and accurately modeled. SAR systems are generally characterized by high spatial resolution, this powerful technique can detect kilometer-scale ionospheric information where such unprecedented spatial resolution was previously inaccessible (e.g., such resolution is 1 to 2 orders of magnitude higher than that obtained by GPS). In this paper, by using the ALOS PALSAR full-polarization data sets, we quantitatively evaluate the reliability and accuracy of retrieved TEC information. We have used the observation results of incoherent scattering radar (ISR) to verify the accuracy of our results, given that ISR is currently the most powerful ground means for ionospheric monitoring and the ideal means for ionospheric data verification. In our work, the AMISR data for the Alaskan region in the United States is selected. After screening the data, we have selected and compared three sets of SAR and ISR results obtained at the same observation time (universal time: 8/6/2010, 21:6:25; 3/19/2011, 7:32:50; 3/31/2011, 7:28:16) and place. Our results show that the deviation between the results of SAR and ISR is only 0.1-0.35 TECU accounting for different factors, such as system and geographical deviations. However, the accuracy of the most widely used GPS data can only up to 1-2 TECU. Both accuracy and resolution of ionospheric sounding using fullpolarization SAR are therefore superior to those of ionospheric sounding using GPS.
Soil moisture is a key component in the hydrologic cycle and climate system. It is an important input parameter for many
hydrologic and meteorological models. NASA’S upcoming Soil Moisture Active Passive (SMAP) mission, to be
launched in October 2014, will address this need by utilizing passive and active microwave measurements at L-band,
which will penetrate moderately dense canopies. In preparation for the SMAP mission, the Soil Moisture Validation
Experiment 2012 (SMAPVEX12) was conducted from 6 June to 17 July 2012 in the Carment-Elm Creek area in
Manitoba, Canada. Over a period of six weeks diverse land cover types ranging from agriculture over pasture and
grassland to forested sites were re-visited several times a week. The Passive/Active L-band Sensor (PALS) provides
radiometer products, vertically and horizontally polarized brightness temperatures, and radar products. Over the past two
decades, successful estimation of soil moisture has been accomplished using passive and active L-band data. However,
remaining uncertainties related to surface roughness and the absorption, scattering, and emission by vegetation must be
resolved before soil moisture retrieval algorithms can be applied with known and acceptable accuracy using satellite
observations. This work focuses on analyzing the Passive/Active L-band Sensor observations of sites covered during
SMAPVEX12, investigating the observed data, parameterizing vegetation covered surface model, modeling inversion
algorithm and analyzing observed soil moisture changes over the time period of six weeks. The data and analysis results
from this study are aimed at increasing the accuracy and range of validity of SMAP soil moisture retrievals via
enhancing the accuracy for soil moisture retrieval.