In the field of remote sensing, panchromatic sharpening technology integrates spatial data from panchromatic images with spectral data from multispectral images to generate high-resolution multispectral images. Precise mapping from multispectral to single-band panchromatic images greatly impacts the quality of fusion images. This paper introduces the spatial-spectral transformer network (SSTNet). SSTNet combines the spatial constraints of the difference model in the gradient domain and the intensity constraints in the spectral domain to quantitatively express multispectral imagery into panchromatic imagery in a many-on-one form, laying the groundwork for designing the loss function in image fusion. compared to the original method, SSTNet is applied to P2Sharpen. Experiments on the Quickbird dataset demonstrate that the evaluation indexes of the SSTNet-based P2Sharpen method are improved in both reduced and full resolution resolution, requiring fewer training data and epoch.
Rapid, accurate extraction of rural residential areas is of great significance to rural planning and urbanization. On the basis of the improved YOLOv8 object detection algorithm, this paper puts forward a technical method for accurately extracting rural residential areas from multi-scale remote sensing images. The rapid extraction of rural blocks comes true via improving the retrieval mechanism of YOLOv8 algorithm: First, the feature extraction module based on ECA local crosschannel interaction attention mechanism is designed to deeply dig the detailed features with inconsistent scales in the detection of residential areas. Efficient channel interaction pays more attention to the positive sample feature information in the feature map, and meanwhile, it reduces the complexity of the model. Second, Swish activation function is proposed to avoid gradient disappearance and poor activation effect caused by over-fitting. Third, DIoU loss is introduced to accurately show the real distance error between two predicted residential areas and enhance the performance of multitarget detection. In the end, ablation experiments and comparative experiments are conducted on CBDV1.0 building data set. The experimental results show that this method can extract rural residential areas from multi-scale remote sensing images, which provides support for large-scale remote sensing image mapping of rural residential areas.
Based on YOLOX network, this paper presented an algorithm for extracting point-like independent houses from remote sensing images. First, an Adaptively Spatial Feature Fusion (ASFF) network was added to the feature extraction module PANet to deeply mine the detailed features of small target houses with different scales. Second, a feature extraction module based on ECA local cross-channel interaction attention mechanism was designed. Efficient channel interaction paid more attention to the positive sample feature information in the feature map and lowered the complicacy of the model. Finally, the Swish activation function was used to avert poor activation effect. Experiments were conducted on the point-like independent houses data set, and the optimum mechanism and effectiveness of the improved method were validated by qualitative analysis of ablation experiments and quantitative analysis of comparison experiments. On the premise of adding ASFF mechanism and ECA attention mechanism and optimizing Swish activation function, the mAP precision of the improved network model was up to 94.83%, 11.16% higher than that of the original network. The robustness and effectiveness of the improved method were quantitatively verified by conducting comparative experiments with widely used detection algorithms.
The dock target in remote sensing images has the characteristics of slender structure and direction arbitrarily. The general target detection algorithm based on the convolutional neural network cannot effectively obtain the direction information of the target, which cannot meet the actual demand of dock detection. This study designed a deep convolutional neural network architecture in any direction based on the YOLOv4 algorithm aimed at resolving the above problems. First, the multidimensional coordinate method was used to calibrate the dock target so that the network could contain the direction information of the target. Second, the loss function of the algorithm was optimized to make it suitable for directional target detection. Finally, an attention mechanism was introduced to enhance the extraction ability of the algorithm and further improve its detection accuracy. Two datasets of dock target detection from remote sensing images were selected for experiments, and the results showed that the improved YOLOv4 network was better than the other networks in the dock target detection task.
Scene classification is an important tool for remote sensing image interpretation, and it has fundamental applications in research and industry. However, given complex backgrounds and scale variations, remote sensing images have large intraclass diversity and interclass similarity, which bring challenges to accurate classification of remote sensing images. We proposed a scene classification method using joint learning and multiscale attention to alleviate the aforementioned problems. To fully utilize the multiscale information of the image and improve the adaptability of the proposed method to objects with various sizes, different from general methods that fuse different scales of features for classification, joint learning using multiscale features is developed to optimize the whole network. Specifically, we leverage a pretrained deep convolutional neural network as the feature extractor to extract low-level, medium-level, and high-level feature maps from the images. Then, due to the poor semantics of low-level and medium-level feature maps compared with the high-level feature maps, we design a multiscale attention module to enhance the semantic information and suppress the noise information. Finally, the global mean pooling is used to obtain the feature vectors and different classifiers are used for different feature vectors. And the decision-level fusion is adopted to obtain more reliable predictions. The experimental results on the AID and NWPU-RESISC45 datasets show that the proposed method makes a significant improvement in terms of overall accuracies compared with the baselines. And the overall accuracies of our method on the two datasets are 97.49% and 95.20%, respectively, which achieves state-of-the-art performance. The code will be public at a Github repository available at https://github.com/Cbanyungong/JLMSAF.
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