Based on the objects’ sparse features, the compressive sensing imaging system has the unique advantage of breaking the Nyquist sampling theorem, and the target image can be reconstructed from very few random coded observations. The system is characterized by simple coding and complex decoding. It is difficult to meet the increasing real-time requirements in application because of the large time consumption by the iterative optimization algorithms. Therefore, it is a powerful way to improve the efficiency by bypassing the complex reconstruction process and extracting the target information directly from the random measurement data. In this paper, based on MNIST handwritten digital character database as an example, the object recognition method from random measurements of compressive sensing camera is explored. Firstly, the training samples in the MNIST database are coded with the observation of the random Bernoulli measurement matrix. And then the K-nearest neighbor classifier is constructed on the standardized samples, the measurements in the same measurement matrix of the target sample are put in the classifier, given the target recognition results. The experimental results show that the average recognition rate is 82.8% under the sampling rate of 0.1, and the total time to process 500 images is 0.063s. In contrast, the experiment of the traditional method by first reconstructing and then recognizing is conducted, the average recognition rate is 84.3%,and the total time to process 500 images is 48.2s. The proposed method is close to the traditional strategy in recognition accuracy, but the computational efficiency has been greatly improved (765 times), with great practical value.
In microwave radar radiometric calibration, the corner reflector acts as the standard reference target but its structure is usually deformed during the transportation and installation, or deformed by wind and gravity while permanently installed outdoor, which will decrease the RCS accuracy and therefore the radiometric calibration accuracy. A fast RCS accuracy measurement method based on 3-D measuring instrument and RCS simulation was proposed in this paper for tracking the characteristic variation of the corner reflector. In the first step, RCS simulation algorithm was selected and its simulation accuracy was assessed. In the second step, the 3-D measuring instrument was selected and its measuring accuracy was evaluated. Once the accuracy of the selected RCS simulation algorithm and 3-D measuring instrument was satisfied for the RCS accuracy assessment, the 3-D structure of the corner reflector would be obtained by the 3-D measuring instrument, and then the RCSs of the obtained 3-D structure and corresponding ideal structure would be calculated respectively based on the selected RCS simulation algorithm. The final RCS accuracy was the absolute difference of the two RCS calculation results. The advantage of the proposed method was that it could be applied outdoor easily, avoiding the correlation among the plate edge length error, plate orthogonality error, plate curvature error. The accuracy of this method is higher than the method using distortion equation. In the end of the paper, a measurement example was presented in order to show the performance of the proposed method.
Calibration and validation (Cal and Val) is one of the most important quality assurance means for satellite payload performance and data quality which has actually restricted RS applicable scope. It has aroused various attentions from academia and industries in recent few decades. The challenges include the lack of consistent RS assessment standard, the uncertainties introduced by atmospheric effect, as well as the gaps in non-synchronous measurements between satellite and field observation. As one of the countries which launched the largest number of earth observation satellites/payloads in last five years, China engaged to solve various challenges of Cal and Val for quantitative RS applications. Several reprehensive works were introduced, including the development of remote sensing technology standardization, the stepwise Cal and Val system, China’s Baotou comprehensive Cal/Val site, automatic in-situ calibration exploration, etc. All these works mitigated the uncertainties of RS measurement and enhanced the precision of quantitative remote sensing.
The compressive sensing imaging technique, based on the realization of random measurement via active or passive
devices (e.g., DMD), has attracted more and more attention. For imaging target of interest within large uniform scene
(e.g., ships in the sea), high-resolution image was usually reconstructed and then used to detect targets, however the
process is time-consuming, and moreover only part of the image consists of the targets of interest. In this paper, the
stepwise multi-resolution fast target detection and imaging method through the combination of different numbers of
DMD mirrors was explored. Low resolution image for larger area target searching and successively higher resolution
image for smaller area containing the targets were reconstructed. Also, non-imaging fast target detection was realized
based on the detector energy intensity, which includes the steps of rough target positioning by successively opening
DMD blocks and accurate target positioning by adjusting the rough areas via intelligent search algorithm. Simulation
experiments were carried out to compare the proposed method with traditional method. The result shows the area of the
ships are accurately positioned without reconstructing the image by the proposed method and the multi-level scale
imaging for suspect areas is realized. Compared with traditional target detection method from the reconstructed image,
the proposed method not only highly enhances the measuring and reconstruction efficiency but also improves the
positioning accuracy, which would be more significant for large area scene.
Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) technique has been demonstrated its success in the
estimation of forest height. Terrain slope is a factor that always affects the estimation accuracy. In this paper, the analysis of terrain influences was carried out in view of polarimetric orientation and local incidence angle shift, respectively. For the former, the relation between forest height estimation error and polarimetric orientation shift was derived by data simulation approach. For the latter, an analytical equation was derived by the theoretical analysis to describe the relation between true and estimated forest height. Then, possible methods for correcting terrain influences were presented, which including: 1) Design airborne experiment flight track along mountain ridge. 2) Utilize Pol-InSAR optimal coherences for forest height inversion if the computational efficiency is not an issue. 3) Revise the estimated forest height from RVoG model inversion, where the range slope can be calculated by InSAR dataset or a priori DEM.
Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) is an emerging technique that combines
interferometric SAR and polarimetric SAR techniques and has shown its effectiveness in the detection of buried weak
targets. The detection performance is affected by the SAR parameters as well as the covering characteristics. In this
paper, the effects of covering characteristics on the detection performance were emphasized and experimentally
investigated by a ground-based Pol-InSAR system. Firstly, the detection principle for buried weak target by Pol-InSAR
technique was presented, which is based on the use of interferometric coherence variation with polarization. Then the
ground-based two dimensional rail (TDR) SAR used for investigation was introduced. Furthermore, the experiment
target scene was designed and the effects of different covering type, different covering moisture, and different covering
depth on the detection performance of weak targets were shown and analyzed. Preliminary results confirmed the
effectiveness of Pol-InSAR technique used for weak target detection and it would be helpful for the further investigation
of this technique.
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