Researchers in the fields of ecological environment and remote sensing pay considerable attention to the estimation of forest canopy height with synthetic aperture radar (SAR). The interest is due to the ability of SAR to penetrate the forest canopy and its sensitivity to forest properties through backscattered intensity. Recent advances in deep learning (DL) present the possibility to derive canopy height maps from single high-resolution (HR) SAR images using neural networks. SAR2Canopy, an innovative framework, is proposed in this paper for canopy height estimation based, which incorporates sensor and scattering knowledge into the estimation. The integration framework allows for the merging of reconstruction trunk scattering features by an equivalent dihedral corner reflector (DCR) scattering model into the supervised tree height estimation process. The proposed method attempts a new approach combining the physical characteristics of SAR data with the nonlinear feature learning ability of DL, potentially extending to different DL algorithms. Experiments are conducted with airborne fully polarimetric P-band SAR data from two areas. Compared to the baseline models, including UNet, DeepLabV3_ResNet50, and FCN_ResNet50, the proposed SAR2Canopy integrated with the DCR model achieves an increase of up to 13.66% in the coefficient of determination (R2), while reducing the root mean squared error (RMSE) and mean absolute error (MAE) by as much as 14.59% and 20.69%, respectively.

In this paper, we propose an edge attention superpixel segmentation method for polarimetric synthetic aperture radar (PolSAR) images. Image edges are specially considered during the segmentation process by imposing an edge-attention-based distance. The edge-attention-based distance is defined on an edge map and it ensure that one superpixel is not divided into several parts by image edges, which helps to generate superpixels with better boundary adherence. Experimental results on AirSAR and Radarsat-2 images demonstrate the superiority of the proposed method.

The inversion of forest structural parameters contributes to evaluation biodiversity and ecosystem functions, as well as enabling sustainable forest management. In this study, we propose a novel method for extracting forest parameters adopting P-band polarimetric synthetic aperture radar (SAR) data. Firstly, we utilize the Freeman-Durden decomposition to extract double-bounce scattering information, including ground-scatterer scattering, scatterer-ground scattering. Then, the scattering amplitude of tree trunks based on the principles of coherent scattering modeling is calculated. Additionally, we introduce the finite cylinder scattering amplitude function and utilize the constraints of the allometric growth model of vegetation to invert forest parameters. The reliability and effectiveness of the proposed method are verified through measurement data, with an RMSE of 3.35m for the inversion results. This research provides a new approach for inverting forest parameters and has potential applications in forest monitoring and ecological studies

Archaeology and Cultural Heritage play a crucial role in fostering the diversity and sustainable development of human culture. Remote sensing data provides valuable insights into underground sites. In this paper, an optical image is employed to classify land cover and extract the Region of Interest (ROI) mask for focused analysis. Moreover, the underground structures are calculated by combining Synthetic Aperture Radar (SAR) data with the Two-Scale Model (TSM). The TSM aids in extracting ground scattering factors while mitigating surface clutter interference. Validation of this method in the Lagash region of southern Iraq demonstrates alignment with known underground structures, as corroborated by published literature.

Above Ground Biomass (AGB) estimation is a basis for rational utilization of natural resources and ecological succession process. Recently, multiple sources of remote sensing data have been used to estimate AGB at high spatial resolution, overcoming the limitations of each type of data. In order to fully exploit the potential of deep learning models based on multi-source data in AGB estimation, an end-to-end Deep Neural Networks (DNN) model is developed using Sentinel-1/2 data, and a learnable weight matrix is designed to tune the contribution of different predictors in multi-source data, thus improving the performance of the model. The experimental results show that the designed DNN model can achieve accurate AGB estimation with a coefficient of determination of 0.7314. Compared with the widely employed XGBoost and Random Forest machine learning models, the proposed DNN model has improved by 6.29% and 5.07% for grassland biomass estimation, respectively.