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Found 10 papers, 3 papers with code
CartoMark: a benchmark dataset for map pattern recognition and 1 map content retrieval with machine intelligence
Maps are fundamental medium to visualize and represent the real word in a simple and 16 philosophical way.
Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping
The integrated results combined both the characteristic of UAV and mobile mapping vehicle point cloud, confirming the practicability of the proposed joint data acquisition platform and the effectiveness of spatio-temporal-spectral-angular observation model.
GLSD: The Global Large-Scale Ship Database and Baseline Evaluations
In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks.
Pan-sharpening via High-pass Modification Convolutional Neural Network
Most existing deep learning-based pan-sharpening methods have several widely recognized issues, such as spectral distortion and insufficient spatial texture enhancement, we propose a novel pan-sharpening convolutional neural network based on a high-pass modification block.
SSCAN: A Spatial-spectral Cross Attention Network for Hyperspectral Image Denoising
In this study, we propose a novel HSI denoising network, termed SSCAN, that combines group convolutions and attention modules.
Unsupervised Remote Sensing Super-Resolution via Migration Image Prior
Despite their success, however, low/high spatial resolution pairs are usually difficult to obtain in satellites with a high temporal resolution, making such approaches in SR impractical to use.
Monitoring urban ecosystem service value using dynamic multi-level grids
Ecosystem valuation is a method of assigning a monetary value to an ecosystem with its goods and services, often referred to as ecosystem service value (ESV).
Region Convolutional Features for Multi-Label Remote Sensing Image Retrieval
In our approach, we first train a FCN model using a pixel-wise labeled dataset, and the trained FCN is then used to predict the segmentation maps of each image in the considered archive.
PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval
Current benchmark datasets are deficient in that 1) they were originally collected for land use/land cover classification and not image retrieval, 2) they are relatively small in terms of the number of classes as well the number of sample images per class, and 3) the retrieval performance has saturated.
Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval
In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNN) for high-resolution remote sensing image retrieval (HRRSIR).
