HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level

Jiang, Jiale; Zhang, Qiaofeng; Yao, Xia; Tian, Yongchao; Zhu, Yan; Cao, Weixing; Cheng, Tao

doi:10.1109/jstars.2020.3016135

Cited by 25 publications

(8 citation statements)

References 56 publications

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“…To the best of our knowledge, very few studies have been conducted to blend satellite images with high spatial resolution. Jiang et al [32] proposed a high-resolution spatiotemporal image fusion (HISTIF) to blend Gaofen-1 images with Sentinel-2 or Landsat images for crop monitoring at a subfield level. Despite the effectiveness of HISTIF, the major processing steps focused on reducing geometrical and spectral mismatches between multi-sensor images, and little attention was paid to reflecting both local details and changes in spatial patterns.…”

Section: As Shown Inmentioning

confidence: 99%

“…However, HIFOW differs from their approach in that change information is directly extracted from multitemporal image segmentation, and residual correction is further applied to complement temporal variations. As the availability of high spatial resolution satellite images increases, it is worth comparing the predictive performance of HIFOW with other STIF models developed for blending multi-sensor high spatial resolution images [32,59].…”

Section: Future Research Directionsmentioning

confidence: 99%

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An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

Park

2022

Agronomy

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Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands.

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Section: As Shown Inmentioning

confidence: 99%

Section: Future Research Directionsmentioning

confidence: 99%

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

Park

2022

Agronomy

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“…However, there are some essential elements that are required for the growth of plants, such as crop life, nutrients, crops health condition, and micronutrients [112,113]. Therefore, monitoring the health condition of each plant of agriculture farming is essential [114]. Therefore, different types of sensors utilize to overcome such issues in smart VF.…”

Section: Sensors Technology In Farming To Monitoring Of the Healthy G...mentioning

confidence: 99%

State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

2021

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The global economy is now under threat due to the ongoing domestic and international lockdown for COVID-19. Many have already lost their jobs, and businesses have been unstable in the Corona era. Apart from educational institutions, banks, privately owned institutions, and agriculture, there are signs of economic recession in almost all sectors. The roles of modern technology, the Internet of things, and artificial intelligence are undeniable in helping the world achieve economic prosperity in the post-COVID-19 economic downturn. Food production must increase by 60% by 2050 to meet global food security demands in the face of uncertainty such as the COVID-19 pandemic and a growing population. Given COVID 19’s intensity and isolation, improving food production and distribution systems is critical to combating hunger and addressing the double burden of malnutrition. As the world’s population is growing day by day, according to an estimation world’s population reaches 9.6 billion by 2050, so there is a growing need to modify the agriculture methods, technologies so that maximum crops can be attained and human effort can be reduced. The urban smart vertical farming (USVF) is a solution to secure food production, which can be introduced at any adaptive reuse, retrofit, or new buildings in vertical manners. This paper aims to provide a comprehensive review of the concept of USVF using various techniques to enhance productivity as well as its types, topologies, technologies, control systems, social acceptance, and benefits. This review has focused on numerous issues, challenges, and recommendations in the development of the system, vertical farming management, and modern technologies approach.

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“…Although Remote Sensing (RS) technology is increasingly achieving remarkable results in practical areas such as crop monitoring, weather forecasting, marine research, and geological surveys [1][2][3][4], as well as land-cover classification, more related research is needed because of the complexity of feature types in some study areas, which easily leads to confusion of samples. Land-cover classification has an extremely important role in tasks such as refined agriculture, land resource exploration, regional geological change, and integrated urban planning [5][6][7][8]. Therefore, accurate access to real-time remote sensing data to improve the accuracy of land-cover classification has been an inevitable need for practical applications.…”

Section: Introductionmentioning

confidence: 99%

MARC-Net: Terrain Classification in Parallel Network Architectures Containing Multiple Attention Mechanisms and Multi-Scale Residual Cascades

Fan

Yan

et al. 2023

Forests

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To address the problem that traditional deep learning algorithms cannot fully utilize the correlation properties between spectral sequence information and the feature differences between different spectra, this paper proposes a parallel network architecture land-use classification based on a combined multi-head attention mechanism and multiscale residual cascade called MARC-Net. This parallel framework is firstly implemented by deeply mining the features generated by grouped spectral embedding for information among spectral features by adding a multi-head attention mechanism, which allows semantic features to have expressions from more subspaces while fully considering all spatial location interrelationships. Secondly, a multiscale residual cascade CNN (convolutional neural network) is designed to fully utilize the fused feature information at different scales, thereby improving the network’s ability to represent different levels of information. Lastly, the features obtained by the multi-head attention mechanism are fused with those obtained by the CNN, and the merged resultant features are downgraded through the fully connected layer to obtain the classification results and achieve pixel-level multispectral image classification. The findings show that the algorithm proposed in this paper has an aggregate precision of 97.22%, compared to that of the Vision Transformer (ViT) with 95.08%; its performance on the Sentinel-2 dataset shows a huge improvement. Moreover, this article mainly focuses on the change rate of forest land in the study area. The Forest land area was 125.1143 km2 in 2017, 105.6089 km2 in 2019, and 76.3699 km2 in 2021, with an increase of 15.59%, an decrease of 0.97%, and increase of 14.76% in 2017–2019, 2019–2021 and 2017–2021, respectively.

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HISTIF: A New Spatiotemporal Image Fusion Method for High-Resolution Monitoring of Crops at the Subfield Level

Cited by 25 publications

References 56 publications

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring

State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

MARC-Net: Terrain Classification in Parallel Network Architectures Containing Multiple Attention Mechanisms and Multi-Scale Residual Cascades

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