With the advancement of remote sensing technology, various new satellite sensors have emerged as the times require. Remote sensing images acquired by different sensors exhibit different characteristics due to their distinct imaging mechanisms. The fusion of Synthetic Aperture Radar (SAR) and optical remote sensing images is valuable for specific remote sensing image applications, as it enables the extraction of texture features from SAR images while preserving the spectral information of optical images. Several existing fusion approaches have been proposed in recent years, including the Nonsubsampled Shearlet Transform Pulse Coupled Neural Network (NSST-PCNN), which is a typical and effective fusion method. However, it suffers from the inconsistency in regional edge information due to the lack of target fusion rules. To address this issue, we propose a new method called MS-NSST-PCNN for multi-model fusion of SAR and optical remote sensing images. This method incorporates the multiScale morphological gradient (MSMG) into NSST-PCNN, which is a technique used to detect edges and enhance the utilization of edge characteristics. The fusion results of two polarization modes, VV and VH are evaluated in combination with existing methods, using image fusion accuracy and visual interpretation criteria. The results demonstrate that for Sentinel 1 and Landsat 8 OLI image fusion the proposed MS-NSST-PCNN method achieves higher correlation coefficients and lower spectral distortion with VV polarization compared to traditional methods in two study areas. Moreover, the proposed method also exhibits better performance for higher spatial resolution GF3 and GF2 images. In subsequent applications of land feature classification, the fusion results of the proposed method achieve higher accuracy than those of other fusion methods or source images applied directly. In the urban and rural application scenarios, the overall classification accuracy of the fusion results can reach 0.87 and 0.88, respectively, which has increased by 8.75% and 23.94% compared with that of using the Landsat8 OLI source images directly.

The utilisation of synthetic aperture radar (SAR) imagery for change detection can effectively circumvents the stringent limitations imposed by weather and lighting conditions, and is finding widespread applications in fields such as disaster monitoring and urban research. To address issues of edge blurring, severe noise interference and sample imbalance, an automated SAR change detection framework is proposed based on enhanced edge information and prototype constrained clustering. Firstly, a gradient-based neighbourhood ratio is designed to reinforce the edge information of the difference map, facilitating robust differential information representations. Subsequently, to obtain accurate samples in an unsupervised manner, we have developed prototype constrained hierarchical clustering for pre-classification. The quantity and quality of selected samples can be precisely guaranteed through the utilisation of histogram analysis and prototype constraints. In the sample learning and prediction phases, a class-balanced noise-tolerant change detection network is proposed that combines focal loss and mean absolute error loss, further tackling the sample imbalance issue, strengthening noise resistance and improving change detection accuracy. Comprehensive experimental results and analysis conducted on five benchmark datasets have validated the effectiveness and robustness of the proposed method.

Change detection based on multi-temporal synthetic aperture radar (SAR) images plays a significant role in environmental and earth observations. With the advancement in deep neural networks, existing work in the literature mainly concentrates on developing self-supervised methods to generate pseudo-labeled samples to guide the subsequent deep learning based detection. However, this way of selecting sample inevitably introduces erroneous labels and imbalance between unchanged and changed classes, thus causing deterioration in change detection performance. To mitigate these issues, we have proposed a SAR change detection network based on visual saliency and multi-hierarchical fuzzy clustering. Specifically, with multi-dimensional difference feature representations, a visual saliency based difference map is constructed for accurate difference feature extraction. By integrating neighborhood information and hierarchical clustering, the multi-hierarchical fuzzy local information C-means clustering algorithm has been developed to identify potential changed regions for sample selection. A class-balanced adaptive focal loss has further been incorporated into the network training to obtain accurate predictions. Extensive experiments and comparisons on five datasets have been performed. The proposed method has achieved averaged accuracy of 99.07% and Kappa coefficient of 79.87%, outperforming other state-of-the-art algorithms both visually and quantitatively.

The existing fire scar detection methods based on multi-temporal analysis only used the difference in either backscattering intensity or polarimetric characteristics between the pre- and post-fire PolSAR data to calculate the difference image (DI), and then applied an object-based image analysis approach (OBIA) to generate the fire scar binary map. These methods all ignored the polarimetric correlation between both the pre- and post-fire PolSAR data. And many parameters of the OBIA method were determined empirically. Therefore, this study proposes a new detection method, which integrates the Hotelling–Lawley trace (HLT) statistic with a hierarchical automatic image segmentation method (GGKI-MRF). The HLT can simultaneously capture changes in both polarimetry and intensity, which helps to distinguish between burned and unburned areas. And the GGKI-MRF method is developed by combining the generalized Gaussian-based Kittler–Illingworth (GG-KI) thresholding with Markov Random Field (MRF) model, which has a good performance in reducing the isolate points (false alarms) and the omission errors. The experimental results from multi-temporal and polarimetric PALSAR PolSAR data show that the proposed method can achieve high detection accuracy (i.e., the kappa coefficient of 0.81 and the overall accuracy up to 0.92).

Characterising spatial information as reinforcement of spectral signatures can largely assist the performance in hyperspectral image (HSI) classification. Markov random fields (MRFs) are probabilistic image texture models, and capable of encoding contextual dependencies through charactering local conditional probabilities. As a representative standardised measure of dispersion of image probability distributions, coefficient of variation (CoV) can be a useful tool for characterising spatial heterogeneity. Their parameter derivation processes also share strong compatibility with convolutional neural networks that specifies spatial correlations in local neighbourhoods. In this work, we propose an MRF and CoV based spectral-spatial convolutional network (MRF-CoV-CNN) for HSI classification. MRF models and CoVs are characterised as measures of spatial distributions and further combined with spectral information. Then the proposed MRF-CoV-CNN takes the fused features as input and produces reliable classification results. Comprehensive experiments have been conducted on the Pavia university dataset and the Salinas dataset to evaluate the proposed method both visually and quantitatively.

Forest height plays a crucial role in various fields, such as forest ecology, resource management, natural disaster management, and environmental protection. In order to obtain accurate and efficient measurements of forest height over large areas, in this study, Terra Synthetic Aperture Radar-X and the TerraSAR-X Add-on for Digital Elevation Measurement (TerraSAR-X/TanDEM-X), Sentinel-2A, and Shuttle Radar Topography Mission (SRTM) data were used, and various feature combinations were established in conjunction with measurements from Light Detection and Ranging (LiDAR). Classification and regression tree (CART), gradient-boosting decision tree (GBDT), random forest (RF), and support vector machine (SVM) algorithms were employed to estimate forest height in the study area. Independent validation on the basis of LiDAR forest height samples showed the following results: (1) Regarding feature combinations, the combination of coherence and decorrelation of volume scattering provided by TerraSAR-X/TanDEM-X data outperformed the combination of backscatter coefficient and local incidence angle, as well as the combination of coherence, decorrelation of volume scattering, backscatter coefficient, and local incidence angle. The best results (R² = 0.67, RMSE = 2.89 m) were achieved with the combination of coherence and decorrelation of volume scattering using the GBDT and RF algorithms. (2) In terms of machine learning methods, the GBDT algorithm proved suitable for estimating forest height. The most effective approach for forest height mapping involved combining the GBDT algorithm with coherence, decorrelation of volume scattering, and a small amount of LiDAR forest height data, used as training data.