12/27/2022 0 Comments Dynamic processes![]() ![]() ![]() Therefore, two methods can be used to preprocess the dual-channel receiving signals, referred to as dual-channel echo reconstruction and dual-channel echo synthesis. The signal can be transmitted through the full antenna without broadening and recorded by each channel. Active phased array antennas that can be divided into two physical channels are equipped on each satellite. LuTan-1 (LT-1) is an innovative spaceborne radar Earth observation mission including two satellites equipped with synthetic aperture radar (SAR) which will be launched in 2022. The proposed technique can be implemented as an optional stage in the processing chain of future staggered SAR missions and leads to improved image quality at a reasonable additional computational cost. The performance of these processing techniques is evaluated through simulations using real TerraSAR-X data. This work proposes processing techniques that mitigate the impact of nadir echoes in staggered SAR through localization and thresholding-and-blanking of these echoes in range-compressed data and recovery of part of the underlying useful signal through interpolation. This PRI variation renders infeasible use of existing methods to avoid nadir echoes, which might impair the quality of staggered SAR images. It uses digital beamforming and a continuous variation of the pulse repetition interval (PRI) to achieve high azimuth resolution over a much wider, continuous swath than is traditionally possible. Staggered SAR is a novel mode of operation under consideration for the next-generation SAR missions such as Tandem-L and NASA-ISRO SAR. Synthetic aperture radar (SAR) is a class of high-resolution imaging radar particularly suitable for satellite remote sensing with diverse applications, such as biomass and ice monitoring, generation of digital elevation models, and measuring of subsidence. Besides that, by reducing the dependency on theoretical models and utilizing the shape, texture, and spatial information embedded in the high-spatial-resolution features, the PolGAN method achieves an RMSE of 2.37 m over the tropical forest site, which is much more accurate than the traditional PolInSAR-based Kapok method (RMSE: 8.02 m). Ablation study is conducted over the boreal site evidencing the superiority of the progressive generator with dual discriminators employed in PolGAN (RMSE: 1.21 m) in comparison with the standard generator with dual discriminators (RMSE: 2.43 m) and the progressive generator with a single coherence (RMSE: 2.74 m) or spatial discriminator (RMSE: 5.87 m). ![]() UAVSAR PolInSAR and LVIS LiDAR data collected over tropical and boreal forest sites are used for experiments. Forest height estimates with high spatial resolution and vertical accuracy are generated through a continuous generative and adversarial process. A tailored Generative Adversarial Network (GAN) called PolGAN with one generator and dual (coherence and spatial) discriminators is proposed to this end, where a progressive pan-sharpening strategy underpins the generator to overcome the significant difference between spatial resolutions of LiDAR and SAR-related inputs. Unlike traditional PolInSAR-based methods, the proposed method reformulates the forest height inversion as a pan-sharpening process between the low-resolution LiDAR height and the high-resolution PolSAR and PolInSAR features. This paper describes a deep-learning-based unsupervised forest height estimation method based on the synergy of the high-resolution L-band repeat-pass Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) and low-resolution large-footprint full-waveform Light Detection and Ranging (LiDAR) data. Analyses of experimental TanDEM-X interferometric data are presented, which demonstrates the potential of the proposed method as a helpful tool for performance budget definition and data rate optimization of present and future SAR missions. This goal is achieved by exploiting the a priori knowledge of the local SAR backscatter statistics, which allows for the generation of high-resolution bitrate maps that can be employed to fulfill a predefined performance requirement. In this article, we introduce the performance-optimized block-adaptive quantization (PO-BAQ), a novel approach for SAR raw data compression that aims at optimizing the resource allocation and, at the same time, the quality of the resulting SAR and InSAR products. In this scenario, the efficient quantization of SAR raw data is of primary importance since the utilized compression rate is directly related to the volume of data to be stored and transmitted to the ground, and at the same time, it affects the resulting SAR imaging performance. For the design of present and next-generation spaceborne SAR missions, constantly increasing data rates are being demanded, which impose stringent requirements in terms of onboard memory and downlink capacity. ![]()
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