Vegetable green coverage estimation from an airborne hyperspectral image

Kosaka, Naoko; Miyazaki, Shuichi; Inoue, Ushio

doi:10.1109/igarss.2002.1026412

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…To improve the quality of classification results, the scientists and researchers used supervised classifier (MLH, ANN, Fuzzy Classifier) to tackle the image processing problem [5,6,11,14,16,30,41]. In the meantime, additional information becomes an appropriate solution to enhance the classification results.…”

Section: Introductionmentioning

confidence: 99%

The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan

Lei

Wan²,

Chou

2007

Comput Geosci

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Texture information offers an extensive solution for image classification by providing better accuracy of image information. However, huge amounts of improper additional texture information may result in a chaotic state, and this leads to uncertainty in the classification process. Considerable portion of earlier works have been carried out through the generally acknowledged procedure of Principal Components Analysis (PCA). However, the PCA method has flaws in the area of influenced and non-influenced attributes. On the whole, whether PCA provides an effective solution to determine the value of knowledge rule in image information still remains a question. This study proposes an innovative method, called Discrete Rough Set method, as a tool for image classification. This study focuses on two crucial issues: (1) The core attributes of the target categories in image classification are systematically analyzed while eliminating surplus attributes rationally; (2) The unique point of each attribute, which influenced the target categories, is successfully found. This is a crucial aspect that is very helpful for the construction of decision rule. Finally, in this study we utilized the expert knowledge classifier and the overall accuracy of Discrete Rough Set (96.67%) exceeds that of the conventional PCA (86.00%) of paddy rice area evaluation from Quickbird image. This result shows that the appropriate classification knowledge can be presented by Discrete Rough Set, and this information can effectively improve the accuracy of image classification.

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Section: Introductionmentioning

confidence: 99%

The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan

Lei

Wan²,

Chou

2007

Comput Geosci

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“…To resolve this problem, extensive studies have been done 624 S. Wan et al through the augmentation of ancillary information for improving the classification accuracy. To improve the quality of classification results, scientists used supervised classifier (MLH, ANN, Fuzzy Classifier) to tackle the problem of image processing (Haralick et al 1973, Sellers 1985, Chica-Olmo and Abarca-Hernández 2000, Yu et al 2000, Kosaka et al 2002, Chou et al 2005, Fang and Liang 2005, Mundt et al 2005. Specifically, some justification for the use of the variogram and madogram as texture measures through neural network approaches would be useful as they play a key part in the procedures outlined (Lloyd et al 2004).…”

Section: Introductionmentioning

confidence: 98%

“…This ancillary information may consist of texture information (Variogram and Fractal dimension, Clarke 1986) and NDVI (Normalized Difference Vegetation Index). A number of strategies are widely used (Haralick et al 1973, Sellers 1985, Chica-Olmo and Abarca-Hernández 2000, Yu et al 2000, Kosaka et al 2002, Chou et al 2005, Fang and Liang 2005. Unfortunately, very few of the studies investigated the efficiency of the texture information.…”

Section: Introductionmentioning

confidence: 99%

An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area

Wan

Lei

Chou

2010

International Journal of Geographical Information Science

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The analysis, measurement, and computation of remote sensing images often require an enhanced supervised classification technique to develop an efficient spatial decision support system. Rice is a crop of global importance, which has drawn a great interest in using remote sensing techniques for evaluating its production. Ancillary information is widely used to improve the classification accuracy of satellite images. However, few of these studies questioned the importance and strategies of using this ancillary information. The enhanced decision support system in our study has two stages. In the first stage, the images are obtained from the remote sensing technique and the ancillary information is employed to increase the accuracy of classification. In the second stage, it is decided to construct an efficiently supervised classifier, which is used to evaluate the ancillary information. Back-propagation neural network (BPN) with extended delta bar delta (EDBD) algorithm is incorporated into our decision support classifier system. This classifier renders two crucial contributions: (1) the EDBD algorithm accelerates the convergence speed of the learning process and (2) the relative importance (RI) on each band of ancillary information is evaluated rationally.

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“…To resolve this problem, extensive studies have carried out the augmentation of ancillary information to improve classification accuracy. To improve the quality of classification results, many scientists have used supervised classifiers (MLH, ANN, and Fuzzy Classifier) to tackle image-processing problems [9,10,[13][14][15][16][17][18]. Specifically, some justification of the use of a variogram and GLCM as texture measures for the optimization of LDA approaches would be useful as they play a key part in the procedures outlined [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Developing a Supervised Spatial Decision Support System for Image Classification: A Study of Paddy Rice Investigation

Chang

2014

Mathematical Problems in Engineering

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Paddy rice area estimation via remote sensing techniques has been well established in recent years. Texture information and vegetation indicators are widely used to improve the classification accuracy of satellite images. Accordingly, this study employs texture information and vegetation indicators as ancillary information for classifying paddy rice through remote sensing images. In the first stage, the images are attained using a remote sensing technique and ancillary information is employed to increase the accuracy of classification. In the second stage, we decide to construct an efficient supervised classifier, which is used to evaluate the ancillary information. In the third stage, linear discriminant analysis (LDA) is introduced. LDA is a well-known method for classifying images to various categories. Also, the particle swarm optimization (PSO) algorithm is employed to optimize the LDA classification outcomes and increase classification performance. In the fourth stage, we discuss the strategy of selecting different window sizes and analyze particle numbers and iteration numbers with corresponding accuracy. Accordingly, a rational strategy for the combination of ancillary information is introduced. Afterwards, the PSO algorithm improves the accuracy rate from 82.26% to 89.31%. The improved accuracy results in a much lower salt-and-pepper effect in the thematic map.

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Vegetable green coverage estimation from an airborne hyperspectral image

Cited by 5 publications

References 4 publications

The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan

The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan

An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area

A Novel Approach to Developing a Supervised Spatial Decision Support System for Image Classification: A Study of Paddy Rice Investigation

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