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Objectives: Interferogram filtering is the key to the subsequent processing steps of interferometric synthetic aperture radar, such as phase unwrapping and geocoding. However, the existing filtering methods can't retain features in dense fringes and accurately estimate phase in low-coherence regions. The convolutional neural networks (CNN) is introduced to learn noise features and solve this problem. Methods: First, we selected a certain number of interferograms as samples, and divided them into training set, test set, and validation set. Second,we preprocessed the training set samples, and cut the preprocessed training set interferograms into small fixed-size block and randomly extract it as a model training sample, used the above steps to train the autoencoder filter model, after a certain number of iterations, the model was fitted. Results: Experiments were carried out on spaceborne imaging radar-C-band synthetic aperture radar data and Sentinel-1A data. Our proposed method was compared with Goldstein filter, mean filter, Lee filter, Frost filter, and improved denoising convolutional neural network (DnCNN). Goldstein filter can remove most of the noise while maintaining fringes edge, has good denoising ability. Mean filter performs well in high-coherence areas, but performs poorly in low-coherence areas, and can't filter noise well. Lee filter maintains it well image resolution, but the denoising effect is weak, and it can be seen from the filtering results that there is still a lot of noise. Frost filtering is weak in low-coherence areas and there is a lot of noise, but the fringe edges are well maintained in high-coherence areas. The improved DnCNN filter can significantly eliminate the noise, but it can't distinguish the fringe edge and the noise well. Our proposed method can suppress the noise very well, and it can maintain the fringe edge well in the low-coherence area and the high-coherence area. Conclusions: This proposed method can greatly improve the phase quality of the interferogram, suppress the noise to a greater extent, and restore more image details and maintain the edge continuity of the interference fringe. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of synthetic aperture radar (SAR) images processing and interpretating in topography, forest vegetation identification, this paper employs the work and progress made by the research group in recent years. Using multi-angle, multi-band, multi-polarization, polarimetric interferometric, and other multi-mode aviation, aerospace SAR data, the terrain features library based on the scattering mechanism are established, and the terrain radiation correction, polarization interfe-rence processing, stereoscopic measurement and knowledge based interpretation model is constructed. The high resolution airborne polarization interference SAR data acquisition system and the SAR image high performance interpretation software system are developed. The principle, technology and method of high confidence processing and interpretation of SAR images with high precision, strong reliability and rich recognition type are realized. At the same time, this paper introduces the application of research results in the surveying and mapping, forestry and other industries, and discusses the problems and solutions in the research. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of DEM (digital elevation model) acquired by airborne InSAR (synthetic aperture radar interferometry) ，we researched the orthogonal decomposition of visual vector model and three dimensional reconstruction model for airborne InSAR. Error sources and adjustment parameters of domestic airborne InSAR system of CASMSAR (synthetic aperture radar system of Chinese Academy of Surveying and Mapping) were analyzed. A block adjustment method was proposed for airborne InSAR which could be used to derive DEM . An Experiment was carried out with X-band interferometric data acquired by CASMSAR. And we used ground control points to correct this error model. The results show that the proposed algorithm could be used to eliminate the differences in plane and height among multi-strip in the overlapping area. Finally，the results met the requirements of topographic mapping at a scale of 1 : 10000 ，and proved the correctness and effectiveness of the new algorithm with CASMSAR data. [ABSTRACT FROM AUTHOR]

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Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)