Remote Sensing Image Target Recognition System Based on Heapsort

Cui, Sidong; Jiang, Zerong; Li, Ping

doi:10.1007/978-981-16-7466-2_120

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…However, when the number of radiation sources is too large, echo signals will overlap, and factors such as ultra-high speed motion and sea clutter will affect the identification results [13] [14] . The ship target recognition technology based on satellite remote sensing image can clearly reflect the color environment of multiple targets, but its acquisition cycle is long, and it cannot recognize the monitored targets in real time [15] . The ship target recognition technology based on visible image [16] can achieve a higher resolution image within the visual range, with low cost and rich colors, and it is not easy to expose the monitoring position.…”

Section: Introductionmentioning

confidence: 99%

Research on ship recognition technology based on infrared image

Chen

Luo

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Infrared ship target recognition technology can automatically detect, analyze and identify ship targets, which is suitable for various types of working environments. This paper takes ship target image as the research object, and measures the effect of existing infrared detection technology, traditional target detection technology and target detection technology based on depth learning through data comparison. For infrared detection and target recognition, its working principle is summarized, and its ability to quickly identify ship types in practical applications is verified. The application and development status of Sea area planning、 supervision and Military reconnaissance field are summarized, and the future development trend is prospected

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Section: Introductionmentioning

confidence: 99%

Research on ship recognition technology based on infrared image

Chen

Luo

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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“…e traditional deserti cation research mainly adopts the arti cial eld mapping method, which has the disadvantages of time-consuming, laborious, and low e ciency in the implementation process [7]. With the emergence of remote sensing technology [9][10][11] and vigorous development of information technology [12][13][14][15][16][17][18], its macro-, objective, and economic advantages make up for the shortcomings of traditional deserti cation research to a certain extent, so it can better deal with environmental change monitoring [16][17][18]. On the other hand, with the development of computer technology, the machine learning method [19][20][21] is widely used in the classi cation and extraction of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Coverage Monitoring Model Design Based on Deep Learning

Zhang

Wang²,

Dong

et al. 2022

Scientific Programming

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Based on the medium-resolution Landsat TM and OLI satellite images in the study area, the deep learning ENVINet-5 model is adopted for vegetation coverage monitoring. By referring to the fusion image and Google Earth high-resolution satellite image, the training samples and verification samples are manually labeled, and the labels of four types of ground objects (desert, water body, cultivated land, and construction land) are made. Through the ENVI deep learning binary classification model, the labeled training samples are trained, and a large number of samples of desert, water, and cultivated land are extracted and transformed into corresponding label images. Then, a large number of training sample labels extracted from the model are combined with the manually made construction land sample labels and both of them are used as the training samples of the ENVI deep learning multiclassification model. According to the classification process of the deep learning model (creating label image, initializing training model, and training model and model classification), through the adjustment of various parameters, the four types of ground objects in the study area are finally classified. Finally, the classification results that meet the accuracy requirements are statistically analyzed. It is proved that the model classification results can meet the use requirements.

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Remote Sensing Image Target Recognition System Based on Heapsort

Cited by 2 publications

References 9 publications

Research on ship recognition technology based on infrared image

Research on ship recognition technology based on infrared image

Vegetation Coverage Monitoring Model Design Based on Deep Learning

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