Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

Wu, Fan; Wang, Chao; Jiang, Shaofeng; Zhang, Hong; Zhang, Bo

doi:10.3390/rs70505511

Cited by 25 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classification was improved by combining Gabor features and LBP features from different perspectives. Wu et al [9] analyzed the reflectivity histogram and estimated the values of some macroscopic features such as length, width and radar cross-sectional profile of the ship, which were evaluated using the fuzzy logic module. Lin et al [10] designed an MSHOG feature describing the ship structure and used a task-driven dictionary learning algorithm to increase the ship separability.…”

Section: A Traditional Handcrafted Feature Methodsmentioning

confidence: 99%

Multifeature Collaborative Fusion Network With Deep Supervision for SAR Ship Classification

Zheng

Liu

et al. 2023

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Multi-feature SAR ship classification aims to build models that can process, correlate, and fuse information from handcrafted features and deep features. Although handcrafted ones provide rich expert knowledge, current fusion methods do not thoroughly investigate the relatively important of handcrafted features with deep features, feature contribution imbalance, and the way features learn collaboratively. In this paper, a novel multi-feature collaborative fusion network with deep supervision (MFCFNet) is proposed to effectively realize handcrafted feature and deep feature fusion in SAR ship classification task. Specifically, our framework mainly includes two types of feature extraction branches, a knowledge supervision and collaboration module, and a feature fusion and contribution assignment module. The former module improves the feature map quality learned by each branch through auxiliary feature supervision, and introduces synergy loss to facilitate the information interaction between deep features and handcrafted features. The latter utilizes an attention mechanism to adaptively balance the importance among various features, and to assign the corresponding feature contribution to the total loss function based on the generated feature weights. We conduct extensive experimental and ablation studies on two public OpenSARShip-1.0 and FUSAR-Ship datasets, and the results show that MFCFNet is effective and outperforms single deep feature and multi-feature models based on previous Internal FC Layer and Terminal FC Layer fusion, and exhibits better performance than the current state-of-the-art methods.

show abstract

Section: A Traditional Handcrafted Feature Methodsmentioning

confidence: 99%

Multifeature Collaborative Fusion Network With Deep Supervision for SAR Ship Classification

Zheng

Liu

et al. 2023

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In 2013, Xing et al [5] combined different structural and scattering features of targets to construct an internal representation, and then used the sparse representation method to classify them. Wu et al [6] proposed a ship classification method in 2015 which was designed to estimate feature vectors by calculating the average value of kernel density estimation, three structural features, and an average backscattering coefficient, and then to classify ships using a support-vector machine model. In 2017, Gorovyi et al [7] proposed the use of Haralick features for texture recognition and local binary patterns as a comparison standard and classified images by fusing azimuth and range target profiles.…”

Section: Related Contentsmentioning

confidence: 99%

“…The total loss is shown in Equation 5, which consists of content loss ℒ 𝑐 and style loss ℒ 𝑠 , where 𝜆 is the weight hyperparameter. The content loss, ℒ 𝑐 , is the two-norm of the contraposition difference obtained by 𝑡 and 𝑡 ′ feature graphs, as shown in (6). The style loss, ℒ 𝑠 , is the two-norm of the mean and standard deviation of each channel of each layer feature map between 𝑡 ′ and s, where 𝑙 is the subscript of the convolutional layer in VGG (see (7)).…”

Section: 𝑇(𝑐 𝑠) = 𝑔(𝑡)mentioning

confidence: 99%

Classifying Ships in SAR Images by Using Contour Bias Features and Transfer Learning

Liu

Yuan

et al. 2022

IEEE Access

View full text Add to dashboard Cite

With the development of synthetic-aperture radar (SAR) image interpretation technology in recent years, classifying detected ships based on SAR images has become an important trend in ocean monitoring. However, owing to the underlying imaging mechanism, the texture of SAR images typically contains noise that cannot be easily eliminated. Therefore, in this study, we consider more stable target contour features to classify SAR images of marine vessels. Based on a deep learning algorithm, we propose a method to obtain contour bias features by employing style transfer learning to classify detected ships from SAR data. We also adopt transfer learning to improve the uneven distribution of SAR datasets of representative ships. The results of experiments conducted to evaluate the proposed approach show that contour bias features improve the generalization performance and classification accuracy of the model. They also show that transfer learning effectively avoids the problem of data imbalance and, thus, improves classification accuracy on the OpenSARShip 2.0 database.

show abstract

“…In the early days, classification of ships from SAR images was explored using polarimetric data [1] and it required an intensive expertise in this domain. With the development and advancement of machine learning (ML) techniques, several studies have 2 developed and tested ML algorithms with handcrafted features such as scattering features [2][3][4], superstructure scattering features [5,6], geometric features [7,8], histogram of oriented gradient (HOG) [9], fusion features [10,11], single-pol COSMO-SkyMed images using the statistical and structural feature vector [12], Although these ML techniques were capable of classifying ships automatically from the SAR images, their performance is limited by the chosen handcrafted features.…”

Section: Introductionmentioning

confidence: 99%

Attention-Guided Convolution Neural Network Assisted With Handcrafted Features for Ship Classification in Low-Resolution Sentinel-1 SAR Image Data

Bhattacharjee,

Shanmugam,

Das

2024

IEEE Access

View full text Add to dashboard Cite

Automatic maritime target classification is essential for effective coastal surveillance and maritime applications. Synthetic aperture radar (SAR) data have been increasingly used for ship classification due to the recent wider open-source data accessibility and the development of deep learning techniques. The convolution neural network (CNN) is a popular deep learning technique used for ship classification using high-resolution labeled SAR image data, despite the limited accessibility of these data due to the substantial cost. The labeled low-resolution SAR image data are less expensive, but their utility is limited due to high false alarms and poor classification accuracy caused by the difficulties in recognizing and discriminating the discrete features for specific classes. To overcome these constraints, we propose an attention-guided CNN model assisted with handcrafted features for ship classification in low-resolution SAR images. This model was trained with OpenSARShip data and tested on Sentinel-1 20 m images with Automatic Identification System (AIS) data class labels from five ports across the world. For the four types of ships (tanker, bulk carrier, container ships and fishing vessels), the experimental results showed an overall classification accuracy of 87.5%, a Kappa score of 0.75, a precision of 86%, and a F1 score of 86.3%. The intercomparison analysis on test data showed statistically a higher performance for the proposed model than for the baseline VGG16 model, which indicates the effectives of the proposed model for various maritime applications.

show abstract

Classification of Vessels in Single-Pol COSMO-SkyMed Images Based on Statistical and Structural Features

Cited by 25 publications

References 23 publications

Multifeature Collaborative Fusion Network With Deep Supervision for SAR Ship Classification

Multifeature Collaborative Fusion Network With Deep Supervision for SAR Ship Classification

Classifying Ships in SAR Images by Using Contour Bias Features and Transfer Learning

Attention-Guided Convolution Neural Network Assisted With Handcrafted Features for Ship Classification in Low-Resolution Sentinel-1 SAR Image Data

Contact Info

Product

Resources

About