Abstract
The development of scientific satellites has made it a reality for people to view the Earth from the sky. However, due to the resolution of the image obtained, the effective and accurate interpretation of remote-sensing images has always been one of the goals pursued by the industry. In this paper, we merge the variable neighborhood search algorithm, reduce the accuracy of remote-sensing images, clean the invalid information of the data, use unsupervised classification methods to quickly locate small targets, use it as verification information, compare and select the image data through sample information, distinguish the background and target results, and get stable detection results. Practice shows that this method can effectively detect small targets in remote-sensing images.
Highlights
With the continuous increase in the number of perception images, remote sensing has become one of the important ways to understand the Earth
Multimedia Detection Method of Small Target Based on Hyperspectral RemoteSensing Image e small target multimedia detection of remote-sensing images is comprehensively analyzed through comprehensive unsupervised classification and support vector machines, and the adaptive feedback determination rules are clarified, including the dimensionality reduction of remote-sensing images, verification information extraction, adaptive adjustment and optimization, and small target multimedia detection and analysis; the specific flowchart is shown in Figure 1: 2.1
From the perspective of accuracy indicators, the variable neighborhood search algorithm has a 1.38% reduction in the detection accuracy of large targets on the verification set, MAPlarge, compared to R-FCN + ResNet101, and other indicators are better than other methods, especially in detecting small targets
Summary
With the continuous increase in the number of perception images, remote sensing has become one of the important ways to understand the Earth. 2. Multimedia Detection Method of Small Target Based on Hyperspectral RemoteSensing Image e small target multimedia detection of remote-sensing images is comprehensively analyzed through comprehensive unsupervised classification and support vector machines, and the adaptive feedback determination rules are clarified, including the dimensionality reduction of remote-sensing images, verification information extraction, adaptive adjustment and optimization, and small target multimedia detection and analysis; the specific flowchart is shown in Figure 1: 2.1. Data are based on principal component analysis to perform dimensionality reduction analysis on remote-sensing image data On this basis, the unsupervised classification method is used to locate small targets in remote-sensing images, which lays a verification foundation for accurate identification, reduces the workload of detection, and improves the efficiency of detection [13,14,15]. MAPRIoU 0.50 and MAPRIoU 0.50 can be calculated by the following formula:
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