Deep Learning-Based Homogeneous Pixel Selection for Multitemporal SAR Interferometry,IEEE Transactions on Geoscience and Remote Sensing

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Deep Learning-Based Homogeneous Pixel Selection for Multitemporal SAR Interferometry
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2022-09-05 , DOI: 10.1109/tgrs.2022.3203975
Jun Hu ₁ , Wenqing Wu ₂ , Rong Gui ₁ , Zhiwei Li ₁ , Jianjun Zhu ₁

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Homogeneous pixel selection (HPS) plays an important role in the application of multitemporal synthetic aperture radar interferometry (InSAR). The statistical goodness-of-fit testing of the temporal samples has been widely used for HPS. However, the detection rates of the existing methods are unsatisfactory under small datasets. In this article, a stacked autoencoder (SAE) network-based method is proposed for the selection of homogeneous pixels under the idea of deep learning image classification, as termed by SAEHPS. The SAE network is used to learn the spatial distribution behavior of the average intensity image. The deep network is trained and tested on different high-resolution SAR datasets of the Hong Kong Airport and the Fuzhou City, and three pixelwise labels (i.e., high, medium, and low reflections) are regarded as outputs of model learning. The unsupervised training and supervised fine-tuning realize the class prediction. The results show that the SAE can achieve robust accuracies above 90% based on empirically labeled samples, especially in nonarchitectural areas where the distributed scatterers exist. The SAE results are devoted to the multitemporal permanent scatterer (PS)/distributed scatterer (DS) InSAR approach to identify homogeneous pixels. Both qualitative and quantitative experiments in HPS, phase optimization, and deformation monitoring have demonstrated the superiority of the novel method.

更新日期：2022-09-05

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