Progressive Data Augmentation Method for Remote Sensing Ship Image Classification Based on Imaging Simulation System and Neural Style Transfer

Qu, Xiao; Liu, Bo; Li, Zengyi; Ni, Wei; Zhang, Yang; Liu, Ligang

doi:10.1109/jstars.2021.3109600

Cited by 26 publications

(15 citation statements)

References 37 publications

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“…Xiao and Liu et al [32] approach can efficiently handle the issues of blurring, poor diversity, and distortion of the output images relative to other data augmentation methods. Despite the lack of realistic images, extensive experimental findings show that this strategy can still be effective.…”

Section: ) Nst With Image Featurementioning

confidence: 99%

An Edge Filter Based Approach of Neural Style Transfer to the Image Stylization

Bagwari¹,

Choudhary²,

Raikwar³

et al. 2022

IEEE Access

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Transferring artistic styles onto any image or photograph has become popular in industry and academia in recent years. The use of neural style transfer (NST) for image style transfer is getting more popular. Convolution Neural Networks (CNN) based style transfer provides a new edge and life to the images, videos, and games. The re-rendering procedure of the content of one image with the style of another using various models and approaches is widely used for image style transfer. However, there are many drawbacks, including image quality, enormous loss, unrealistic artefacts, and the style of localized regions being less compared to the desired artistic style. For the reason that transfer technique fails to capture detailed, miniature textures and keep the true artwork's texture scales. We propose a multimodal CNN that stylizes hierarchically with several losses of increasing sizes while considering faithful representations of both colour and luminance channels. We may transfer not only large-scale, evident style cues but also subtle, exquisite ones by effectively handling style and texture cues at different sizes using various modalities. Our approach providing aesthetically pleasing results and is more comparable to multiple desirable creative styles using colour and texture cues at different scales.

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Section: ) Nst With Image Featurementioning

confidence: 99%

An Edge Filter Based Approach of Neural Style Transfer to the Image Stylization

Bagwari¹,

Choudhary²,

Raikwar³

et al. 2022

IEEE Access

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“…Consequently, they fail in enhancing the semantic information fidelity of RS images, particularly when deployed in tasks demanding nuanced interpretation like CD [12]. To overcome these limitations, some works have leveraged imaging simulation systems to generate synthetic RS samples, which are subsequently combined with original data [13]. These innovative methodologies effectively address concerns such as data diversity, blurriness, and distortions.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Adversarial Learning-Based Change Detection in Imbalanced Datasets Using Artificial Image Generation and Attention Mechanism

Oubara,

Wu,

Maleki

et al. 2024

IJGI

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Deep Learning (DL) has become a popular method for Remote Sensing (RS) Change Detection (CD) due to its superior performance compared to traditional methods. However, generating extensive labeled datasets for DL models is time-consuming and labor-intensive. Additionally, the imbalance between changed and unchanged areas in object CD datasets, such as buildings, poses a critical issue affecting DL model efficacy. To address this issue, this paper proposes a change detection enhancement method using artificial image generation and attention mechanism. Firstly, the content of the imbalanced CD dataset is enhanced using a data augmentation strategy that synthesizes effective building CD samples using artificial RS image generation and building label creation. The created building labels, which serve as new change maps, are fed into a generator model based on a conditional Generative Adversarial Network (c-GAN) to generate high-resolution RS images featuring building changes. The generated images with their corresponding change maps are then added to the CD dataset to create the balance between changed and unchanged samples. Secondly, a channel attention mechanism is added to the proposed Adversarial Change Detection Network (Adv-CDNet) to boost its performance when training on the imbalanced dataset. The study evaluates the Adv-CDNet using WHU-CD and LEVIR-CD datasets, with WHU-CD exhibiting a higher degree of sample imbalance compared to LEVIR-CD. Training the Adv-CDNet on the augmented dataset results in a significant 16.5% F1-Score improvement for the highly imbalanced WHU-CD. Moreover, comparative analysis showcases the superior performance of the Adv-CDNet when complemented with the attention module, achieving a 6.85% F1-Score enhancement.

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“…Image-to-image translation (I2IT) [1] is proposed to visually transform images of one style into another and has attracted a great deal of attention due to its extensive application in the fields of style transfer [2], image colorization [3], remote sensing [4][5][6], target detection [7], data representation [8,9], underwater image restoration [10], medical image processing [11,12], haze removal [13] and noise removal [14], etc. Following several years of development, researchers have found that generative adversarial networks [15] and their variant models are effective solutions for most image translation tasks and obtain very impressive results in both supervised and unsupervised [16,17] settings.…”

Section: Introductionmentioning

confidence: 99%

Binary Noise Guidance Learning for Remote Sensing Image-to-Image Translation

Zhang,

Zhou,

Zheng

et al. 2023

Remote Sensing

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Image-to-image translation (I2IT) is an important visual task that aims to learn a mapping of images from one domain to another while preserving the representation of the content. The phenomenon known as mode collapse makes this task challenging. Most existing methods usually learn the relationship between the data and latent distributions to train more robust latent models. However, these methods often ignore the structural information among latent variables, leading to patterns in the data being obscured during the process. In addition, the inflexibility of data modes caused by ignoring the latent mapping of two domains is also one of the factors affecting the performance of existing methods. To make the data schema stable, this paper develops a novel binary noise guidance learning (BnGLGAN) framework for image translation to solve these problems. Specifically, to eliminate uncertainty of domain distribution, a noise prior inference learning (NPIL) module is designed to infer an estimated distribution from a certain domain. In addition, to improve the authenticity of reconstructed images, a distribution-guided noise reconstruction learning (DgNRL) module is introduced to reconstruct the noise from the source domain, which can provide source semantic information to guide the GAN’s generation. Extensive experiments fully prove the efficiency of our proposed framework and its advantages over comparable methods.

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Progressive Data Augmentation Method for Remote Sensing Ship Image Classification Based on Imaging Simulation System and Neural Style Transfer

Cited by 26 publications

References 37 publications

An Edge Filter Based Approach of Neural Style Transfer to the Image Stylization

An Edge Filter Based Approach of Neural Style Transfer to the Image Stylization

Enhancing Adversarial Learning-Based Change Detection in Imbalanced Datasets Using Artificial Image Generation and Attention Mechanism

Binary Noise Guidance Learning for Remote Sensing Image-to-Image Translation

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