The utilization of advanced remote sensing methods to monitor the coastal wetlands is essential for conservation and sustainable development. With multiple polarimetric channels, the polarimetric synthetic aperture radar (PolSAR) is increasingly employed in land cover classification and information extraction, as it has more scattering information than regular SAR images. Polarimetric decomposition is often used to extract scattering information from polarimetric SAR. However, distinguishing all land cover types using only one polarimetric decomposition in complex ecological environments such as coastal wetlands is not easy, and thus integration of multiple decomposition algorithms is an effective means of land cover classification. More than 20 decompositions were used in this research to extract polarimetric scattering features. Furthermore, a new algorithm combining random forest (RF) with sequential forward selection (SFS) was applied, in which the importance values of all polarimetric features can be evaluated quantitatively, and the polarimetric feature set can be optimized. The experiments were conducted in the Jiangsu coastal wetlands, which are located in eastern China. This research demonstrated that the classification accuracies were improved relative to regular decision tree methods, and the process of polarimetric scattering feature set optimization was intuitive. Furthermore, the scattering matrix elements and scattering features derived from $\mathrm{H}/\mathsf{\alpha }$ , Yamaguchi3, VanZyl3, and Krogager decompositions were determined to be very supportive of land cover identification in the Jiangsu coastal wetlands.

Remote sensing images play a critical role in urban planning, land resources and environmental monitoring. Land cover classification is one of the straightforward applications of remote sensing. However, the anomalous remote sensing data challenges the reliability of land cover classification results. Deep learning has been widely used in remote sensing image analysis, but it remains sensitive to anomalous data. To address this issue, we re-evaluate a land cover classification map in high-noise scenarios with anomalous data and propose a novel network architecture to solve the problem. A new network architecture is proposed to solve this problem. Our proposed network architecture focuses on decoupling the extraction of global information and local information. Through three global-local feature fusion modules, we output features emphasizing global information, features emphasizing local information, and consistency evaluation scores, respectively. A specially designed decoder integrates these three features. Our method performs better compared to mainstream models on the public datasets WHDLD with obvious anomaly data, with a MIoU of 63.58% and a Mpa of 74.32%. Compared to the suboptimal method, our method improves MIoU by 1.29% and Mpa by 3.05%.

Tremendous groundwater withdrawal has caused land subsidence disaster in Su-Xi-Chang area, China. In order to monitor land subsidence and reduced the potential damages to railway and highway, nn effective monitoring method is urgently needed. SBAS-InSAR method with SAR data provides an effective ability to detected land subsidence in recent years. In this work, we monitor land subsidence area along railway and highway in Su-Xi-Chang area from 2007 to 2012 by using SBAS-InSAR processing method with ALOS PALSAR SAR data. The InSAR result along highway and highway ranged from -50 mm/year to 16 mm/year, -50 mm/year to 26 mm/year, respectively. Analysis of land subsidence along highway in Wuxi area showes that the area with serious land subsidence distributes in the industrial concentrated area..