Pansharpening is a critical yet challenging low-level vision task that aims to obtain a higher-resolution image by fusing a multispectral (MS) image and a panchromatic (PAN) image. While most pansharpening methods are based on convolutional neural network (CNN) architectures with standard convolution operations, few attempts have been made with context-adaptive/dynamic convolution, which delivers impressive results on high-level vision tasks. In this paper, we propose a novel strategy to generate local-context adaptive (LCA) convolution kernels and introduce a new global harmonic (GH) bias mechanism, exploiting image local specificity as well as integrating global information, dubbed LAGConv. The proposed LAGConv can replace the standard convolution that is context-agnostic to fully perceive the particularity of each pixel for the task of remote sensing pansharpening. Furthermore, by applying the LAGConv, we provide an image fusion network architecture, which is more effective than conventional CNN-based pansharpening approaches. The superiority of the proposed method is demonstrated by extensive experiments implemented on a wide range of datasets compared with state-of-the-art pansharpening methods. Besides, more discussions testify that the proposed LAGConv outperforms recent adaptive convolution techniques for pansharpening.
Pansharpening is an important yet challenging remote sensing image processing task, which aims to reconstruct a high-resolution (HR) multispectral (MS) image by fusing a HR panchromatic (PAN) image and a low-resolution (LR) MS image. Though deep learning (DL)-based pansharpening methods have achieved encouraging performance, they are infeasible to fully utilize the deep semantic features and shallow contextual features in the process of feature fusion for a HR-PAN image and LR-MS image. In this paper, we propose an efficient full-depth feature fusion network (FDFNet) for remote sensing pansharpening. Specifically, we design three distinctive branches called PAN-branch, MS-branch, and fusion-branch, respectively. The features extracted from the PAN and MS branches will be progressively injected into the fusion branch at every different depth to make the information fusion more broad and comprehensive. With this structure, the low-level contextual features and high-level semantic features can be characterized and integrated adequately. Extensive experiments on reduced- and full-resolution datasets acquired from WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate that the proposed FDFNet only with less than 100,000 parameters performs better than other detail injection-based proposals and several state-of-the-art approaches, both visually and quantitatively.
The dynamic programming algorithm is characterized by high computational efficiency and global optimization in trajectory planning. In 3D trajectory planning, as the spatial search space expands, the number of grid points increases faster, and time complexity of the dynamic programming algorithm is O(n 3 ). It often leads to a "Curse of Dimension" phenomenon, which lowers its computational efficiency drastically. To solve this problem, this paper divides the entire planning space into stages based on Bellman's optimality principle. A dynamic programming-genetic algorithm(DPGA) is proposed by using genetic algorithm(GA) in each stage for optimization, while using dynamic programming algorithm(DP) in global planning. The global optimization ability of the algorithm is verified through convergence analysis. Moreover, based on a series of simulation experiments, it shows that the improved algorithm proposed in this paper is more efficient than the dynamic programming algorithm and genetic algorithm alone in global optimization.INDEX TERMS trajectory planning, dynamic programming algorithm, genetic algorithm;
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