Classification performance assessment in side scan sonar image while underwater target object recognition using random forest classifier and support vector machine

Kumudham, R.; Rajendran, V. G.

doi:10.14419/ijet.v7i2.21.12448

Cited by 8 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Random Forest (RF) is a robust ensemble learning algorithm that combines decision trees through bagging and random subspace methods [ 30 , 31 ]. It is adept at handling high-dimensional, noisy data for classification and regression tasks, providing variable importance measures for feature contribution [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Addressing Challenges in Port Depth Analysis: Integrating Machine Learning and Spatial Information for Accurate Remote Sensing of Turbid Waters

Li,

Wu,

Shen

2024

Sensors

View full text Add to dashboard Cite

Bathymetry estimation is essential for various applications in port management, navigation safety, marine engineering, and environmental monitoring. Satellite remote sensing data can rapidly acquire the bathymetry of the target shallow waters, and researchers have developed various models to invert the water depth from the satellite data. Geographically weighted regression (GWR) is a common method for satellite-based bathymetry estimation. However, in sediment-laden water environments, especially ports, the suspended materials significantly affect the performance of GWR for depth inversion. This study proposes a novel approach that integrates GWR with Random Forest (RF) techniques, using longitude, latitude, and multispectral remote sensing reflectance as input variables. This approach effectively addresses the challenge of estimating bathymetry in turbid waters by considering the strong correlation between water depth and geographical location. The proposed method not only overcomes the limitations of turbid waters but also improves the accuracy of depth inversion results in such complex aquatic settings. This breakthrough in modeling has significant implications for turbid waters, enhancing port management, navigational safety, and environmental monitoring in sediment-laden maritime zones.

show abstract

Section: Methodsmentioning

confidence: 99%

Addressing Challenges in Port Depth Analysis: Integrating Machine Learning and Spatial Information for Accurate Remote Sensing of Turbid Waters

Li,

Wu,

Shen

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Random Forest (RF) is a robust ensemble learning algorithm that combines decision trees through bagging and random subspace methods (Kumudham and Rajendran 2018;Liu et al 2015).. It's adept at handling high-dimensional, noisy data for classification and regression tasks, providing variable importance measures for feature contribution (Liaw and Wiener 2002).…”

Section: Random Forests (Rf)mentioning

confidence: 99%

Integrating Geolocational Features with ZY-1E Satellite and Multibeam Data Using Random Forest Algorithm for Bathymetry Inversion in the Yangtze River Nantong Channel

zhongqiang,

wei,

xin

et al. 2024

Preprint

View full text Add to dashboard Cite

Accurate bathymetry information is crucial for safe navigation and efficient management of the Yangtze River Channel, a vital shipping corridor in China. Traditional bathymetric surveying methods are time-consuming and labor-intensive, limiting their application in large-scale and real-time monitoring. This study proposes a novel approach for bathymetry inversion in the Yangtze River Nantong Channel by integrating geolocational features from the ZY-1E satellite with high-resolution multibeam data using a random forest algorithm. The Random Forest with Longitude/Latitude (RF-Lon./Lat.) model, which incorporates geographical information, outperformed conventional methods, achieving an R² of 0.57, MAE of 1.99m, and RMSE of 2.96m. The successful application of the RF-Lon./Lat. model highlights the effectiveness of integrating geolocational features with machine learning algorithms for accurate bathymetry inversion in the complex and turbid waters of the Yangtze River Channel. This innovative approach offers a promising solution for precise and efficient water depth estimation, which is essential for various applications in the Yangtze River Basin, including channel management, waterway maintenance, and hydrological studies. The insights gained from this study contribute to the growing body of knowledge on the application of machine learning and remote sensing techniques for bathymetric mapping in complex river environments, particularly in the context of the Yangtze River Channel.

show abstract

“…(3) y pq = pq r 00 (4) When the image is distorted due to different shooting angles, the translation, scale and rotation invariance of Hu moment invariants can not meet the actual requirements, and a moment invariance under the condition of affine transformation of the target is needed to deal with distortion and other deformations [16].…”

Section: Feature Extraction Of Multi-source Remote Sensing Imagementioning

confidence: 99%

A Target Recognition Algorithm of Multi-Source Remote Sensing Image Based on Visual Internet of Things

Sun

Lin

2022

Mobile Netw Appl

View full text Add to dashboard Cite

Multi-source remote sensing images have the characteristics of large differences in texture and gray level. Mismatch and low recognition accuracy are easy to occur in the process of identifying targets. Thus, in this paper, the target recognition algorithm of multi-source remote sensing image based on IoT vision is investigated. The infrared sensor and SAR radars are set in the visual perception layer of the iVIOT. The visual perception layer transmits the collected remote sensing image information to the application layer through the wireless networks. The data processing module in the application layer uses the normalized central moment idea to extract the features of multi-source remote sensing image. Contourlet two-level decomposition is performed on the image after feature extraction to realize multi-scale and multi-directional feature fusion. A two-step method of primary fineness is used to match the fused features and the random sampling consensus algorithm is used to eliminate false matches for obtaining the correct match pairs. After the image feature matching is completed, the BVM target detection operator is used to complete the target recognition of multi-source remote sensing image. Experimental results show that the use of the IoT to visually recognizing the desired remote sensing image target has low communication overhead, and the recognition reaches 99% accuracy.

show abstract

Classification performance assessment in side scan sonar image while underwater target object recognition using random forest classifier and support vector machine

Cited by 8 publications

References 2 publications

Addressing Challenges in Port Depth Analysis: Integrating Machine Learning and Spatial Information for Accurate Remote Sensing of Turbid Waters

Addressing Challenges in Port Depth Analysis: Integrating Machine Learning and Spatial Information for Accurate Remote Sensing of Turbid Waters

Integrating Geolocational Features with ZY-1E Satellite and Multibeam Data Using Random Forest Algorithm for Bathymetry Inversion in the Yangtze River Nantong Channel

A Target Recognition Algorithm of Multi-Source Remote Sensing Image Based on Visual Internet of Things

Contact Info

Product

Resources

About