A novel study on ant-based clustering for paddy rice image classification

Chang, Shih-Hsun; Wan, Shiuan

doi:10.1007/s12517-014-1617-2

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…However, it cannot perform well on complex distributions. Those models may include the infinite Gaussian mixture models (GMM) [10], the hidden naive Bayes model (NBM) [10], and the hidden Markov models (HMM) [11]. An auto-encoder has a structure similar to multi-layer perceptron (MLP) which has the primary difference when using an unsupervised data for the number of neurons in the output layer is equal to the number of inputs [12].…”

Section: Introductionmentioning

confidence: 99%

The Study of Intelligent Image Classification Systems: An Exploration of Generative Adversarial Networks with Texture Information on Coastal Driftwood

Yeh,

Wan,

et al. 2023

Environments

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Taiwan is located at the junction of plates in which the stratum is relatively unstable, resulting in frequent earthquakes. Driftwood has always been regarded as a precious asset that enables ecoscientists to track earthquakes. In the event of a typhoon or heavy rain, the surface water flows to flush the woods from the hills to the coast. More specifically, a large rainfall or earthquake may cause floods and collapses, and the trees in the forest will be washed down. Therefore, this study used high-resolution images to build an image database of the new north coast of Taiwan, and a deep learning approach is incorporated to classify the driftwoods. To improve the interpretation of driftwood in the remote images, we initially import eight pieces of textured information which are employed to the raw bands (B, G, R, and IR). The usage of spatial information image extraction technology is incorporated into a deep learning analysis using two parallel approaches. The generative adversarial network (GAN) is used to analyze the color images alongside an ancillary image with texture information. Most of the salt–pepper effects are produced by applying a high-resolution thematic map, and an error matrix is generated to compare the differences between them. The raw data (original R + G + B + IR) images, when analyzed using GAN, have about 70% overall classification outcomes. Not all of the driftwood can be detected. By applying the texture information to the parallel approach, the overall accuracy is enhanced to 78%, and about 80% of the driftwood can be recognized.

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Section: Introductionmentioning

confidence: 99%

The Study of Intelligent Image Classification Systems: An Exploration of Generative Adversarial Networks with Texture Information on Coastal Driftwood

Yeh,

Wan,

et al. 2023

Environments

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“…In addition, the increase in artificial intelligence methods has created an alternative field of study to classify these features in a different way. Although such studies in the agricultural field are abundant in the literature, new methods, [10][11][12] hybrid approaches 13,14 still attract attention. The general purpose is high quality and low error, as required by precision farming.…”

Section: Introductionmentioning

confidence: 99%

Bread and durum wheat classification using wavelet based image fusion

2020

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BACKGROUND: Wheat, which is an essential nutrient, is an important food source for human beings because it is used in flour and feed production. As in many nutrients, wheat plays an important role in macaroni and bread production. The types of wheat used for both foods are different, namely bread and durum wheat. A strong separation of these two wheat types is important for product quality. This article differs from the traditional methods available for the identification of bread and durum wheat species. In this study, ultraviolet (UV) and white light (WL) images of wheat are obtained for both species. Wheat types in these images are classified by various machine learning (ML) methods. Afterwards, these images are fused by waveletbased image fusion method. RESULTS: The highest accuracy value calculated using only UV and only WL image is 94.8276% and these accuracies are obtained by Support Vector Machine (SVM) and multilayer perceptron (MLP) algorithms, respectively. However, this accuracy value is 98.2759% for the fusion image and both MLP and SVM achieved the same success. CONCLUSION: Wavelet-based fusion has increased the classification accuracy of all three learning algorithms. It is concluded that the identification ability in the resulting fusion image is higher than the other two raw images.

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“…Many efforts have been made to map paddy rice planting areas by using various classification algorithms and data sources, including optical- and microwave-based remotely sensed data. In terms of classification approaches, they can generally be divided into unsupervised classification [ 10 , 11 ] and supervised classification methods [ 12 , 13 ]. Knowledge- [ 14 ] and phenology-based approaches [ 15 ] are typical methods used in supervised classification.…”

Section: Introductionmentioning

confidence: 99%

Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China

Tian

Wang

et al. 2018

Sensors

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Areas and spatial distribution information of paddy rice are important for managing food security, water use, and climate change. However, there are many difficulties in mapping paddy rice, especially mapping multi-season paddy rice in rainy regions, including differences in phenology, the influence of weather, and farmland fragmentation. To resolve these problems, a novel multi-season paddy rice mapping approach based on Sentinel-1A and Landsat-8 data is proposed. First, Sentinel-1A data were enhanced based on the fact that the backscattering coefficient of paddy rice varies according to its growth stage. Second, cropland information was enhanced based on the fact that the NDVI of cropland in winter is lower than that in the growing season. Then, paddy rice and cropland areas were extracted using a K-Means unsupervised classifier with enhanced images. Third, to further improve the paddy rice classification accuracy, cropland information was utilized to optimize distribution of paddy rice by the fact that paddy rice must be planted in cropland. Classification accuracy was validated based on ground-data from 25 field survey quadrats measuring 600 m × 600 m. The results show that: multi-season paddy rice planting areas effectively was extracted by the method and adjusted early rice area of 1630.84 km2, adjusted middle rice area of 556.21 km2, and adjusted late rice area of 3138.37 km2. The overall accuracy was 98.10%, with a kappa coefficient of 0.94.

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A novel study on ant-based clustering for paddy rice image classification

Cited by 4 publications

References 21 publications

The Study of Intelligent Image Classification Systems: An Exploration of Generative Adversarial Networks with Texture Information on Coastal Driftwood

The Study of Intelligent Image Classification Systems: An Exploration of Generative Adversarial Networks with Texture Information on Coastal Driftwood

Bread and durum wheat classification using wavelet based image fusion

Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China

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