Feature extraction and matching of remote sensing images is becoming increasingly important with a wide range of applications. It matches and superimposes images obtained from the same scene at different times, different sensors, and different angles, and maps the optimal to the target image. CNN-based algorithms have shown superior expressiveness compared to traditional methods in almost all fields with image. This paper optimises the network based on SuperPoint by replacing convolution with a depth-separable convolution which has smaller number of parameters, and replacing the conv block with a spindle-type Inverted Residuals block composed of dimension expansion, depth-separable convolution and Dimension reduction. The network depth is fine-tuned to ensure accuracy. The model is trained on the RSSCN7 remote sensing dataset. Compared with other traditional algorithms in a cross-sectional manner with the combination of SuperGlue, the optimized algorithm shows the superior comprehensive performance.
The task of segmenting small infrared targets, which have few pixels and weak features, has been a difficult problem in the field of small target image processing. Small targets exist not only in general images, but also widely in UAV cameras, communication base station cameras, rescue cameras and vehicle cameras. The study of small target segmentation algorithms is very important for analyzing and utilizing these images, and has important applications in security, transportation, and rescue. Traditional small target segmentation algorithms are able to segment objects with simple target contour edges and large differences in signal strength. The traditional algorithm often has high false detection rate and missed detection rate when facing several targets with weak signal strength, and does not perform well in complex scenes. In this paper, we introduce an infrared small target segmentation scheme facing multiple types and numbers of targets. We also produce an infrared UAV and pedestrian dataset for validation.
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