Comparison of classification accuracy of co-located hyperspectral &amp; multispectral images for agricultural purposes

Bostan, Selin; Ortak, Mehmet Akif; Tuna, Caglayan; Akoguz, Alper; Sertel, Elif; Ustundag, Burak Berk

doi:10.1109/agro-geoinformatics.2016.7577671

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…The higher spectral resolution of hyperspectral sensors compared to multispectral data clearly enhances vegetation application accuracy. For example, Hyperion outperformed Landsat when classifying crops, while they both have a 30-m GSD [60]. Thenkabail et al [61] also concluded that Hyperion performed more accurate rainforest classification than ALI, IKONOS or ETM+.…”

Section: Preliminary Analysis Of the Literature Databasementioning

confidence: 99%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi-and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors' publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors' applications with Sentinel-2's and (ii) analyzes the applications' requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

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Section: Preliminary Analysis Of the Literature Databasementioning

confidence: 99%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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“…The authors reported better performances of using hyperspectral imagery than using Landsat imagery for both research purposes. Similarly, Bostan et al [42] compared Hyperion and Landsat images for crop classification and found that higher classification accuracy can be achieved by using hyperspectral imagery. [43].…”

Section: Satellite Mounted Sensorsmentioning

confidence: 99%

Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land

Misbah¹,

Laamrani²,

Khechba³

et al. 2021

Remote Sensing

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Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.

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“…Hyperspectral imaging involves the acquisition of a series of images, where each pixel contains reflectance spectra ranging from visible and infrared (VNIR: 400-1000 nm) to short-wavelength infrared (SWIR: 1000-1700 nm), typically consisting of dozens or hundreds of channels. Because they contain both spectral and spatial information, hyperspectral images (HSIs) are ideal for applications in various fields including remote sensing [2], cancer detection [3], agricultural crop classification [4], cultural heritage preservation [5].…”

Section: Introductionmentioning

confidence: 99%

ATSFCNN: a novel attention-based triple-stream fused CNN model for hyperspectral image classification

Cai,

Boust,

Mansouri

2024

Mach. Learn.: Sci. Technol.

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Recently, the Convolutional Neural Network (CNN) has gained increasing importance in hyperspectral image classification thanks to its superior performance. However, most of the previous research has mainly focused on 2D-CNN, and the limited applications of 3D-CNN have been attributed to its complexity, despite its potential to enhance information extraction between adjacent channels of the image. Moreover, 1D-CNN is typically restricted to the field of signal processing as it ignores the spatial information of hyperspectral images. In this paper, we propose a novel CNN model named ATSFCNN (Attention-based Triple-Stream Fused Convolutional Neural Network) that fuses the features of 1D-CNN, 2D-CNN, and 3D-CNN to consider all the relevant information of the hyperspectral dataset. Our contributions are twofold: First, we propose a strategy to extract and homogenize features from 1D, 2D, and 3D CNN. Secondly, we propose a way to efficiently fuse these features. This attention-based methodology adeptly integrates features from the triple streams, thereby transcending the former limitations of singular stream utilization. Consequently, it becomes capable of attaining elevated outcomes in the context of hyperspectral classification, marked by increased levels of both accuracy and stability. We compared the results of ATSFCNN with those of other deep learning models, including 1D-CNN, 2D-CNN, 2D-CNN+PCA, 3D-CNN, and 3D-CNN+PCA, and demonstrated its superior performance and robustness. Quantitative assessments, predicated on the metrics of Overall Accuracy (OA), Average Accuracy (AA), and Kappa Coefficient (κ) emphatically corroborate the preeminence of ATSFCNN. Notably, spanning three remote sensing datasets, ATSFCNN consistently achieves peak levels of Overall Accuracy, quantified at 98.38%, 97.09%, and 96.93% respectively. This prowess is further accentuated by concomitant Average Accuracy scores of 98.47%, 95.80%, and 95.80%, as well as Kappa Coefficient values amounting to 97.41%, 96.14%, and 95.21%.

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Comparison of classification accuracy of co-located hyperspectral & multispectral images for agricultural purposes

Cited by 8 publications

References 6 publications

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land

ATSFCNN: a novel attention-based triple-stream fused CNN model for hyperspectral image classification

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