The variation of Link quality in underwater sensor networks often lead to link interruption and void-hole. To address the above issues, some protocols chose nodes with good link quality as relay nodes without considering fairness, while others used multipath and greedy forwarding which cause high energy consumption. Therefore, this article proposes a fair and energy efficient cooperative routing protocol based on link quality for underwater acoustic sensor network (FECRP). This protocol divides underwater sensor networks into clusters, selects cluster heads and relay nodes based on perceived link quality, it cooperatives with MAC protocol in the data transmission within the cluster, scheduling the repeated transmission of data packets to overcome link interruptions. The simulation results show that this protocol improves fairness and reduces energy consumption while ensuring throughput.

Considering the limitations of stereo satellite resources and imaging quality, as well as the challenges of occlusion and disparity discontinuity impacting the matching results, this paper proposes a novel generalized stereo matching method based on topological structure consistency for urban 3D reconstruction. Firstly, the topological structure within the corresponding superpixel neighborhood of the left and right views is constructed. Then, the topological structure consistency cost (TSC) is proposed to evaluate the similarity of the topological structure by combining two measurement methods of grayscale spatial distance and spatial relative relationship. Finally, iteratively optimizing the matching cost through visibility term and disparity discontinuity term to output the disparity map. The experimental results show that the proposed stereo matching method achieves normalized median absolute deviation (NMAD) better than 1.5 m and root mean square error (RMSE) better than 3.5 m, which can realize more advanced performance compared with the state-of-the-art methods.

Addressing the challenge of multi-class change detection with a small sample size, a change vector analysis with adaptive weight Gaussian mixture model (CVA-AWGMM) is proposed in this article. Initially, in order to avoid errors caused by geometric distortion and imprecise alignment, we employed a neighborhood search approach when calculating the different map. Instead of direct pixel-to-pixel comparisons, we compensated corresponding pixels in the images before and after the change by finding the minimum difference within a specified pixel range. Following this, we employ the fundamental framework of CVA to extract magnitude and angle features from the change vectors, facilitating the identification of both changed and unchanged regions through threshold segmentation. Based on the above binary change detection result, a semi-supervised Gaussian mixture model is used for further change class differentiation. Recognizing the inherent challenges of effectively training classifier model with a small sample size, the clustering features in a large number of unlabeled samples and the supervised information from a few labeled samples are simultaneously utilized to co-construct the objective function of the model. Meanwhile, considering the complementary properties of magnitude and angle features, the labeled samples are used to adaptively weight the features of both to further improve the accuracy of the method. Experiments were conducted on two public data sets and one self-made data set, and the results demonstrate that the proposed CVA-AWGMM outperforms several typical methods.

Low-quality digital surface model (DSM) and blurred textures result in poor visualization of building reconstructions. In this paper, an edge-based block decomposition method for building rooftop reconstruction and side walls generation is proposed, which is applied in three-dimensional (3D) reconstruction texture mapping of buildings. The rooftop is decomposed into blocks according to the contour of the building's rooftop in the orthophoto. Each block is combined with the DSM generated by the stereo pair of satellite images to reconstruct the rooftop and interpolate to generate the side walls to obtain a regularized 3D point cloud of the building. For better texture mapping effect, single image super-resolution (SISR) method is used to enhance the texture details of remote sensing images. Experimental results show that the proposed method has better visualization effect than other 3D reconstruction methods based on satellite data.

Accurate and timely crop mapping is essential for agricultural applications, and deep-learning methods have been applied on a range of remotely sensed data sources to classify crops. In this article, we develop a novel crop classification method based on spatiotemporal deep-learning fusion technology. However, for crop mapping, the selection and labeling of training samples is expensive and time consuming. Therefore, we propose a fully automated training-sample-selection method. First, we design the method according to image processing algorithms and the concept of a sliding window. Second, we develop the Geo-3D convolutional neural network (CNN) and Geo-Conv1D for crop classification using time-series Sentinel-2 imagery. Specifically, we integrate geographic information of crops into the structure of deep-learning networks. Finally, we apply an active learning strategy to integrate the classification advantages of Geo-3D CNN and Geo-Conv1D. Experiments conducted in Northeast China show that the proposed sampling method can reliably provide and label a large number of samples and achieve satisfactory results for different deep-learning networks. Based on the automatic selection and labeling of training samples, the crop classification method based on spatiotemporal deep-learning fusion technology can achieve the highest overall accuracy (OA) with approximately 92.50% as compared with Geo-Conv1D (91.89%) and Geo-3D CNN (91.27%) in the three study areas, indicating that the proposed method is effective and efficient in multi-temporal crop classification.