Using object-based deep learning for the urban land cover classification has become a mainstream method. This study proposed an urban land cover classification method based on segments’ object features, deep features and spatial association features. The proposed method used the synthetic semivariance function to determine the hyperparameters of the superpixel segmentation and subsequently optimized the image superpixel segmentation result. A convolutional neural network (CNN) and a graph convolutional neural network (GCN) were used to obtain segments’ deep features and spatial association features respectively. The random forest algorithm was used to classify segments based on the multi-dimension features. The results showed that the image superpixel segmentation results had the significant impact on the classification results. Compared with the pixel-based method, the segment-based methods generally yielded the higher classification accuracy. The strategy of multi-dimension feature fusion can combine the advantages of each singledimension feature to improve the classification accuracy. The proposed method utilizing multi-dimension features was more efficient than traditional methods used for the urban land cover classification. The fusion of segments’ object features, deep features and spatial association features was the best solution for achieving the urban land cover classification.

Optical remote sensing images were prone to extensive cloud coverage, especially under mountainous conditions with frequent weather changes. To address this issue, this paper proposed a novel cloud removal method that integrated features from SAR and neighboring optical remote sensing images. The method was based on a deep CGAN network, leveraging both local and global features of SAR images as well as edge features of optical remote sensing images to perform coarse cloud removal. Building upon the coarse cloud removal, the spectral features of neighboring optical remote sensing images were utilized for refined cloud removal. The experimental results showed that the proposed coarse-to-fine cloud removal method achieved a satisfactory cloud removal performance for optical remote sensing images. The RMSE, SAM, mSSI, and CC values were 0.0391, 0.0729, 0.9221, and 0.9537, respectively. Compared with other classical methods, each of these four metrics improved by at least 0.0119, 0.0438, 0.0217, and 0.0240, respectively. Moreover, the proposed method demonstrated superior performance in terms of boundary smoothness, cloud shadow elimination, spectral consistency, and spatial detail restoration for cloud removal in images with different underlying surface conditions. The effectiveness of the proposed method will improve the quality of cloud removal in optical remote sensing images.

To improve the accuracy and efficiency of rice recognition in mountainous areas, this paper proposes a feature selection method combining the Gray Wolf Optimization (GWO) algorithm and the Random Forest classifier. First, the Relief algorithm is applied to further filter the initial feature set by calculating feature weights and setting appropriate thresholds, optimizing the feature subset's dimensionality and classification accuracy. Then, the GWO algorithm is used to further optimize the feature subset, reducing feature redundancy and correlation, and improving the quality of the final feature set. The GWO algorithm simulates the hunting behavior of gray wolf packs, dynamically adjusting the positions of the wolves to gradually approach the optimal solution. During the optimization process, the Random Forest classifier serves as the fitness function to evaluate the classification accuracy of each feature subset. Through an iterative update process, the GWO_RF algorithm successfully selects the optimal feature subset, achieving high recognition accuracy with reduced computational complexity. Experimental results show that the proposed method significantly improves recognition accuracy and effectively reduces computation time, providing a new approach for the efficient analysis of large-scale remote sensing images.

Rice is one of the most important staple crops in China, and the rapid and accurate extraction of rice planting areas plays a crucial role in the agricultural management and food security assessment. However, the existing rice field identification methods faced the significant challenges in mountainous regions due to the severe cloud contamination, insufficient utilization of multi-dimensional features, and limited classification accuracy. This study presented a novel rice field identification method based on the Graph Convolutional Networks (GCN) that effectively integrated multi-source remote sensing data tailored for the complex mountainous terrain. A coarse-to-fine cloud removal strategy was developed by fusing the synthetic aperture radar (SAR) imagery with temporally adjacent optical remote sensing imagery, achieving high cloud removal accuracy, thereby providing reliable and clear optical data for the subsequent rice mapping. A comprehensive multi-feature library comprising spectral, texture, polarization, and terrain attributes was constructed and optimized via a stepwise selection process. Furthermore, the 19 key features were established to enhance the classification performance. The proposed method achieved an overall accuracy of 98.3% for the rice field identification in Huoshan County of the Dabie Mountains, and a 96.8% consistency compared to statistical yearbook data. The ablation experiments demonstrated that incorporating terrain features substantially improved the rice field identification accuracy under the complex topographic conditions. The comparative evaluations against support vector machine (SVM), random forest (RF), and U-Net models confirmed the superiority of the proposed method in terms of accuracy, local performance, terrain adaptability, training sample requirement, and computational cost, and demonstrated its effectiveness and applicability for the high-precision rice field distribution mapping in mountainous environments.