Intelligence fusion method research of multisource high-resolution remote sensing images

Li, Shengyang; Zhang, W.; Yang, Song

doi:10.11834/jrs.20176386

Cited by 3 publications

(3 citation statements)

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“…Under the influence of the study areas with different surface complexities and different data sources, the parameter settings needed to be explored accordingly. In addition, in the application of a spatiotemporal fusion model for the study of the surface vegetation cover [36], there were those who performed the fusion after the waveform calculation, and those who calculated the vegetation index after the waveform fusion [37]. The different timing of the fusion also had an impact on the accuracy of the fusion results.…”

Section: Discussionmentioning

confidence: 99%

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

Fan

Huang

et al. 2023

Sustainability

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The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the flexible spatiotemporal data fusion (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR.

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Section: Discussionmentioning

confidence: 99%

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

Fan

Huang

et al. 2023

Sustainability

View full text Add to dashboard Cite

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“…The remaining principal components (PC2 to PCn) after noise removal are PCA inverted and fused with the panchromatic image to restore it to the original color space. This process results in the generation of a fused high-resolution image [9][10][11][12]. The transformation formula is shown in Eq.…”

Section: Pca Based Hyperspectral Image Fusion Methodsmentioning

confidence: 99%

Hyperspectral low altitude UAV target tracking algorithm based on deep learning and improved KCF

Sun,

Ma,

et al. 2024

Front. Phys.

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This article presents a novel target tracking algorithm for hyperspectral low altitude UAV, combining deep learning with an improved Kernelized Correlation Filter (KCF). Initially, an image noise reduction method based on principal component analysis with Block-Matching 3D (BM3D), is employed to process redundant information. Subsequently, an image fusion method is utilized to merge the processed hyperspectral image and the high-resolution panchromatic band image to obtain a high spatial resolution image for target enhancement. Following this, YOLOv5 is used to detect the coordinate information of the UAV target in the current frame. Then, The KCF algorithm is used for target tracking in the current frame using kernel correlation filtering. Finally, the Discriminative Scale Spatial Tracker (DSST) is employed to determine the scale information to achieve a multi-scale tracking effect. The experimental results demonstrate that the algorithm presented in this paper surpasses CSK, HLT, and the conventional KCF algorithm in hyperspectral UAV datasets. On average, there is a significant increase in accuracy which is over 17% when using our algorithm.

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“…The unique value of remote sensing big data also promotes the wide application of remote sensing technology in the fields of land resource, urban planning, agroforestry meteorology, ecological environment, disaster reduction, and defense security [15]. The research on remote sensing big data has mainly oriented to the specific applications involving information exaction [19,20], data retrieval [21,22], data fusion [23,24] and data mining [25,26]. In a broad sense, remote sensing big data consists of data from satellite remote sensing and ground sensor networks, which can reflect the characteristics of earth-surface environment, and data from social sensing equipment, such as smart phones, navigation devices, wearable devices, video surveillance devices, etc., which can illustrate the patterns in human activities, and social and economic forms.…”

Section: Related Workmentioning

confidence: 99%

An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective

Zhu

et al. 2023

Remote Sensing

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With the development and popularization of remote sensing earth observation technology and the remote sensing satellite system, the problems of insufficient proactiveness, relevance and timeliness of large-scale remote sensing supporting services are increasingly prominent, which seriously restricts the application of remote sensing resources in multi-domain and cross-disciplinary. It is urgent to help terminal users make appropriate decisions according to real-time network environment and domain requirements, and obtain the optimal resources efficiently from the massive remote sensing resources. In this paper, we propose a recommendation algorithm using fusion of attention and multi-perspective (MRS_AMRA). Based on MRS_AMRA, we further implement an active service recommendation model (MRS_ASRM) for massive multi-source remote sensing resources by combining streaming pushing technology. Firstly, we construct value evaluation functions from multi-perspective in terms of remote sensing users, data and services to enable the adaptive provision of remote sensing resources. Then, we define multi-perspective heuristic policies to support resource discovery, and fusion these policies through the attention network, to achieve the accurate pushing of remote sensing resources. Finally, we implement comparative experiments to simulate accurate recommendation scenarios, compared with state-of-the-art algorithms, such as DIN and Geoportal. Furthermore, MRS_AMRA achieves an average improvement of 10.5% in the recommendation accuracy NDCG@K, and in addition, we developed a prototype system to verify the effectiveness and timeliness of MRS_ASRM.

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Intelligence fusion method research of multisource high-resolution remote sensing images

Cited by 3 publications

References 0 publications

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

Hyperspectral low altitude UAV target tracking algorithm based on deep learning and improved KCF

An Active Service Recommendation Model for Multi-Source Remote Sensing Information Using Fusion of Attention and Multi-Perspective

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