NDVI-Net: A fusion network for generating high-resolution normalized difference vegetation index in remote sensing

Zhang, Hao; Ma, Jiayi; Chen, Chen; Tian, Xin

doi:10.1016/j.isprsjprs.2020.08.010

Cited by 50 publications

(22 citation statements)

References 35 publications

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“…Multispectral satellite images are the most popular images that are processed with At-DL methods (81 papers) [91,92,134] (Figure 11). This is mostly due to the free availability of some MS satellite images and their wide range of applications.…”

Section: Rq4 What Are the Used Data Sets/types In Attention-based Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

“…This is a surprisingly low number; however, due to the very high resolution of the UAV images, the attention mechanism could significantly increase the performance of the DL methods. Multispectral satellite images are the most popular images that are processed with At-DL methods (81 papers) [91,92,134] (Figure 11). This is mostly due to the free availability of some MS satellite images and their wide range of applications.…”

Section: Rq4 What Are the Used Data Sets/types In Attention-based Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

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Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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Section: Rq4 What Are the Used Data Sets/types In Attention-based Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

Section: Rq4 What Are the Used Data Sets/types In Attention-based Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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“…According to the characteristics of ASQ-Discover, we selected NDVI (normalized difference vegetation index), NDRE (normalized difference red edge), BNDVI (blue normalized difference vegetation index), GNDVI (green normalized difference vegetation index), OSAVI (optimized soil-adjusted vegetation index) and TGI (triangular greenness index), and generate the high resolution NDVI, NDRE, BNDVI, GNDVI, OSAVI, and TGI images. The NDVI is an indicator to measure the photosynthetic activity of vegetation [29], and it is frequently employed as a proxy for plant greenness or vegetation growth [30]. The OSAVI is based on the NDVI but includes correction factors for the soil reflectance in the spectra, and it can reflect the result of cumulative water deficits [31,32].…”

Section: Selecting Vegetation Indicesmentioning

confidence: 99%

A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging

Zhang

et al. 2021

Agriculture

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Monitoring grassland vegetation growth is of vital importance to scientific grazing and grassland management. People expect to be able to use a portable device, like a mobile phone, to monitor grassland vegetation growth at any time. In this paper, we propose a handheld grassland vegetation monitoring system to achieve the goal of monitoring grassland vegetation growth. The system includes two parts: the hardware unit is a hand-held multispectral imaging tool named ASQ-Discover based on a smartphone, which has six bands (wavelengths)—including three visible bands (450 nm, 550 nm, 650 nm), a red-edge band (750 nm), and two near-infrared bands (850 nm, 960 nm). The imagery data of each band has a size of 5120 × 3840 pixels with 8-bit depth. The software unit improves image quality through vignetting removal, radiometric calibration, and misalignment correction and estimates and analyzes spectral traits of grassland vegetation (Fresh Grass Ratio (FGR), NDVI, NDRE, BNDVI, GNDVI, OSAVI and TGI) that are indicators of vegetation growth in grassland. We introduce the hardware and software unit in detail, and we also experiment in five pastures located in Haiyan County, Qinghai Province. Our experimental results show that the handheld grassland vegetation growth monitoring system has the potential to revolutionize the grassland monitoring that operators can conduct when using a hand-held tool to achieve the tasks of grassland vegetation growth monitoring.

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“…The first is that the low correlation between the PAN and MS images may cause spectral distortion [30]. The second is that vegetated and non-vegetated regions are rarely differentiated when determining the injection gains, which will cause spatial and spectral distortion [33].…”

Section: Introduction Emote Sensing Images Which Are Obtained By Dete...mentioning

confidence: 99%

Pansharpening Based on Variational Fractional-Order Geometry Model and Optimized Injection Gains

Yang

Huang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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NDVI-Net: A fusion network for generating high-resolution normalized difference vegetation index in remote sensing

Cited by 50 publications

References 35 publications

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging

Pansharpening Based on Variational Fractional-Order Geometry Model and Optimized Injection Gains

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