Accurate extraction of offshore raft aquaculture areas based on a 3D-CNN model

Jiang, Zongchen; Ma, Yi

doi:10.1080/01431161.2020.1737340

Cited by 19 publications

(10 citation statements)

References 26 publications

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“…Additionally, based on the trained classi-fier's accuracy, weight is assigned to it in each iteration. [36] The more accurate classifier will be given more weight. This method iterates until the entire training set fits perfectly, or until the stated maximum number of estimators has been reached [37] to categorize the voting algorithm created for the selection.…”

Section: Inception V3mentioning

confidence: 99%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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A significant amount of effort and cost is required to collect training samples for remote sensing image classifications. The study of remote sensing and how to read multispectral images is becoming more important. High-dimensional multispectral images are created by the various bands that show how materials behave. The need for more information about things and the improvement of sensor resolutions have led to the creation of multispectral data with a higher size. In recent years, it has been shown that the high dimensionality of these data makes it hard to preprocess them in multiple ways. Recent research has demonstrated that one of the most crucial methods to address this issue is by adopting a variety of learning strategies. But as the data gets more complicated, these methodologies are not adequate to support. The proposed methodology shows that the classification experiment using remote sensing images indicates the maximum likelihood classifier with different deep learning models; weight vector (WV) AdaBoost and ADAM can greatly limit overfitting, and it obtains high classification accuracy. Proposed VGG16 and Inception v3 increase classification accuracy along with optimization process produce 96.08%.

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Section: Inception V3mentioning

confidence: 99%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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“…Grey-level Co-occurrence Matrix (GLCM) was the most commonly used method to extract texture information from images [25], and it was proved useful in aquaculture area classification [10], [14]. In this study, we first used principal components analysis (PCA) [26] to generate the first few principal component layers which indicated over 99% of the information of the input Sentinel-2 images.…”

Section: Ndvi = (Nir -R) / (Nir + R)mentioning

confidence: 99%

“…In recent decades, optical satellite images especially multispectral images such as GaoFen-2 [10], Landsat 8 [11], and Sentinel-2 images [2] have been widely used in aquaculture mapping. Synthetic aperture radar (SAR) image such as Sentinel-1 is another RS data type that has been applied to aquaculture classification [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China

Han

Huang

Ling

et al. 2023

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

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Freshwater aquaculture in Jianghan Plain plays a key role in the whole industry of China. It was expanding rapidly to meet the fast-growing demand of consumption in these decades. The spatial distribution change of aquaculture in Jianghan Plain has attracted many researchers in recent years and it is worth further investigating. However, the accuracy and the quality of inland aquaculture classification and mapping still have space to be improved. Our study attempts to use Sentinel-1 and Sentinel-2 data to classify aquaculture areas, non-aquaculture water, and non-water areas. We applied image segmentation and pixel-based feature computation to obtain candidate datasets. Recursive feature elimination (RFE) was used to find the optimal datasets which were then imported into a modified UNet for classification. This workflow was then applied to the annual datasets for better observation of the spatial change of aquaculture areas. In total 12 indices and features were found most influential and preserved after RFE. The overall classification accuracy reached 96.83%, while the precision and recall of aquaculture areas reached 84.47% and 90.48%, respectively. The combination of object-based and pixel-based image analysis showed its advantage in inland aquaculture classification compared to other studies. Integrated rice-crawfish farming was found a key factor motivating the rapid development of aquaculture. Policies about the fishing ban and ecosystem restoration were also found deeply affecting the spatial change of aquaculture in Jianghan Plain.

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“…In contrast to traditional multi-spectral remote sensing technology, it can obtain rich oil-spill spectral characteristic information [28][29][30]. Moreover, deep learning has developed rapidly in recent years because of its powerful ability to extract features from high-dimensional data [31][32][33][34][35][36][37]. Deep networks and multi-level features fusion method for deep learning have been applied to hyperspectral image classification, and research progress has been made [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

Jiang

Zhang

et al. 2021

Remote Sensing

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Marine oil spills can damage marine ecosystems, economic development, and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses and scientific decision-making. Optical remote sensing is an important method for marine oil-spill detection and identification. In this study, hyperspectral images of five types of oil spills were obtained using unmanned aerial vehicles (UAV). To address the poor spectral separability between different types of light oils and weak spectral differences in heavy oils with different thicknesses, we propose the adaptive long-term moment estimation (ALTME) optimizer, which cumulatively learns the spectral characteristics and then builds a marine oil-spill detection model based on a one-dimensional convolutional neural network. The results of the detection experiment show that the ALTME optimizer can store in memory multiple batches of long-term oil-spill spectral information, accurately identify the type of oil spills, and detect different thicknesses of oil films. The overall detection accuracy is larger than 98.09%, and the Kappa coefficient is larger than 0.970. The F1-score for the recognition of light-oil types is larger than 0.971, and the F1-score for detecting films of heavy oils with different film thicknesses is larger than 0.980. The proposed optimizer also performs well on a public hyperspectral dataset. We further carried out a feasibility study on oil-spill detection using UAV thermal infrared remote sensing technology, and the results show its potential for oil-spill detection in strong sunlight.

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Accurate extraction of offshore raft aquaculture areas based on a 3D-CNN model

Cited by 19 publications

References 26 publications

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China

Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer

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