MF-SRCDNet: Multi-feature fusion super-resolution building change detection framework for multi-sensor high-resolution remote sensing imagery

Li, Shaochun; Wang, Yanjun; Cai, Hengfan; Lin, Yunhao; Wang, Mengjie; Teng, Fei

doi:10.1016/j.jag.2023.103303

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…The other type of CD dataset is constructed by multiple sources of images, such as unmanned aerial vehicle (UAV) images and syntheses images. The WXCD dataset [17] is handcrafted from UAV and SuperView-1 (SV-1) images as the original image in the RGB band, and the building areas with significant changes in two temporal images are manually annotated as vectors using ArcGIS software 10.2 and converted to the Tiff format. The unique natural and human environment of the site, its complex and diverse building forms and scenes, and the sensor characteristics of the bitemporal images with different shooting angles and lighting variations pose a greater challenge to the building CD task than other publicly available datasets.…”

Section: Rsi Datasets For Change Detection (Cd)mentioning

confidence: 99%

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Li,

Zhou,

Zhang

et al. 2024

Remote Sensing

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Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land.

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Section: Rsi Datasets For Change Detection (Cd)mentioning

confidence: 99%

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Li,

Zhou,

Zhang

et al. 2024

Remote Sensing

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“…Machine Learning can find better solutions by analyzing and learning from historical and behavioral data and can simplify many complicated manual tasks. As a branch of machine learning, deep learning [25,26] is now used in various fields, such as face recognition, traffic safety [27], agricultural pests [28], and geographic research [29]. Image segmentation is one of the popular fields of computer vision.…”

Section: Quantitative Research On Street Spacementioning

confidence: 99%

A Spatial Analysis of Urban Streets under Deep Learning Based on Street View Imagery: Quantifying Perceptual and Elemental Perceptual Relationships

Sun,

Xu,

et al. 2023

Sustainability

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Measuring the human perception of urban street space and exploring the street space elements that influence this perception have always interested geographic information and urban planning fields. However, most traditional efforts to investigate urban street perception are based on manual, usually time-consuming, inefficient, and subjective judgments. This shortcoming has a crucial impact on large-scale street spatial analyses. Fortunately, in recent years, deep learning models have gained robust element extraction capabilities for images and achieved very competitive results in semantic segmentation. In this paper, we propose a Street View imagery (SVI)-driven deep learning approach to automatically measure six perceptions of large-scale urban areas, including “safety”, “lively”, “beautiful”, “wealthy”, “depressing”, and “boring”. The model was trained on millions of people’s ratings of SVIs with a high accuracy. First, this paper maps the distribution of the six human perceptions of urban street spaces within the third ring road of Wuhan (appearing as Wuhan later). Secondly, we constructed a multiple linear regression model of “street constituents–human perception” by segmenting the common urban constituents from the SVIs. Finally, we analyzed various objects positively or negatively correlated with the six perceptual indicators based on the multiple linear regression model. The experiments elucidated the subtle weighting relationships between elements in different street spaces and the perceptual dimensions they affect, helping to identify the visual factors that may cause perceptions of an area to be involved. The findings suggested that motorized vehicles such as “cars” and “trucks” can negatively affect people’s perceptions of “safety”, which is different from previous studies. We also examined the influence of the relationships between perceptions, such as “safety” and “wealthy”. Finally, we discussed the “perceptual bias” issue in cities. The findings enhance the understanding of researchers and city managers of the psychological and cognitive processes behind human–street interactions.

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“…Super-resolution (SR) is an advanced image processing technique designed to significantly enhance the resolution of images, enabling the restoration of intricate HR details from either single or consecutive LR images. In recent years, CNN has made remarkable progress in the field of SR, which has also made SR technology widely used [12]. For example, in the field of remote sensing, to reduce the domain difference between the simulated data set and the displayed world, self-supervised degradation-guided adaptive networks and contrastive learning of remote sensing image SR in a semi-supervised environment are proposed, thereby eliminating the need for implement vision tasks with large amounts of data [13,14].…”

Section: Introductionmentioning

confidence: 99%

A novel facial age estimation through super-resolution based on deep learning

Yao,

He,

Ding

et al. 2024

Meas. Sci. Technol.

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Age estimation encounters challenges due to the low resolution (LR) of images captured in real-world scenarios, emphasizing the critical role of image quality in achieving accurate age estimations. Despite efforts to enhance age estimation through LR image training, existing methodologies often overlook the incorporation of super-resolution (SR) as a crucial pre-processing step for LR images. This study introduces an innovative approach that integrates SR of facial images with age estimation methodologies. Specifically, a Hierarchical Reconstruction Network structure is employed to train LR images, fully integrating shallow concrete features and deep abstract features. Validation of the training outcomes includes evaluation metrics such as Peak Signal-to-Noise Ratio and Structural Similarity Index Measurement, showing superior performance compared to several state-of-the-art approaches. Subsequently, an Enhanced Soft Stage Regression Network is designed for facial age measurement and trained using the SR-IMDB dataset generated by Hierarchical Reconstruction Network to minimize mean absolute error. This approach achieves a final result of 7.33, indicating an average performance improvement of 10.94% compared to five other state-of-the-art methods. Experimental results highlight the effectiveness of integrating SR into the age estimation process.

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MF-SRCDNet: Multi-feature fusion super-resolution building change detection framework for multi-sensor high-resolution remote sensing imagery

Cited by 11 publications

References 24 publications

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

A Spatial Analysis of Urban Streets under Deep Learning Based on Street View Imagery: Quantifying Perceptual and Elemental Perceptual Relationships

A novel facial age estimation through super-resolution based on deep learning

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