Research on Ground Object Classification Method of High Resolution Remote-Sensing Images Based on Improved DeeplabV3

Ground-object classification using remote-sensing images of high resolution is widely used in land planning, ecological monitoring, and resource protection. Traditional image segmentation technology has poor effect on complex scenes in high-resolution remote-sensing images. In the field of deep lear...

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Vydané v:Sensors (Basel, Switzerland) Ročník 22; číslo 19; s. 7477
Hlavní autori: Fu, Junjie, Yi, Xiaomei, Wang, Guoying, Mo, Lufeng, Wu, Peng, Kapula, Kasanda Ernest
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.10.2022
MDPI
Predmet:
California
China
Datasets
Educational objectives
Environmental monitoring
high-resolution remote-sensing images
Image processing
Methods
Natural resources
Neural networks
object classification
Protection and preservation
Remote sensing
Satellites
semantic segmentation
Semantics
China
California
ISSN:1424-8220, 1424-8220
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Shrnutí:Ground-object classification using remote-sensing images of high resolution is widely used in land planning, ecological monitoring, and resource protection. Traditional image segmentation technology has poor effect on complex scenes in high-resolution remote-sensing images. In the field of deep learning, some deep neural networks are being applied to high-resolution remote-sensing image segmentation. The DeeplabV3+ network is a deep neural network based on encoder-decoder architecture, which is commonly used to segment images with high precision. However, the segmentation accuracy of high-resolution remote-sensing images is poor, the number of network parameters is large, and the cost of training network is high. Therefore, this paper improves the DeeplabV3+ network. Firstly, MobileNetV2 network was used as the backbone feature-extraction network, and an attention-mechanism module was added after the feature-extraction module and the ASPP module to introduce focal loss balance. Our design has the following advantages: it enhances the ability of network to extract image features; it reduces network training costs; and it achieves better semantic segmentation accuracy. Experiments on high-resolution remote-sensing image datasets show that the mIou of the proposed method on WHDLD datasets is 64.76%, 4.24% higher than traditional DeeplabV3+ network mIou, and the mIou on CCF BDCI datasets is 64.58%. This is 5.35% higher than traditional DeeplabV3+ network mIou and outperforms traditional DeeplabV3+, U-NET, PSP-NET and MACU-net networks.
Bibliografia:ObjectType-Article-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22197477