Spatial knowledge databases as applied to the detection of changes in urban land use

Chou, Tien-Yin; Lei, Tsu Chiang; Wan, Shiuan; Liang, Yang

doi:10.1080/01431160500057889

Cited by 20 publications

(11 citation statements)

References 9 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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“…After selecting the training sites and checking the statistics, a signature file was created on the basis of the information obtained about the distribution of LU/LC features in the image through an initial field reconnaissance and unsupervised classification. The thumb rule given by Chou et al (2005), that minimum 50 pixels per training set and 10 samples per class should be chosen, was adopted. Results of unsupervised classification and facts collected from field visits were incorporated during selection of training sites for supervised classification.…”

Section: Lu/lc Mapmentioning

confidence: 99%

Soil loss estimation through MUSLE using Kirpich and Williams times of concentration using RS and GIS techniques: a case study

Kamath

Varun

Dwarakish³

et al. 2012

ISH Journal of Hydraulic Engineering

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Water is one of the most vital requirements for sustenance of life. Water, along with soil, is the most essential natural resource for economic and social development. A study of soil and water dynamics at a watershed level can facilitate a scientific approach toward their conservation and management. The present study is an attempt to apply the Modified Universal Soil Loss Equation (MUSLE) along with the Soil Conservation Service Curve Number (SCS-CN) method for runoff estimation and comparison of soil loss estimates for the catchments of Baindur Hole and Yedamavina Hole in the Udupi District of Karnataka State, India, obtained by times of concentration calculated by the Kirpich equation and the Williams equation. The base map and thematic maps were prepared using Indian Remote Sensing satellite 1C (IRS-1C) LISS-III (Linear Imaging Self-Scanning Sensor) image for land use/land cover and from Survey of India topo sheet in a GIS environment for overlaying and extraction of results. The time of concentration estimated by the Kirpich formula is lower in all cases; hence, the corresponding soil loss is 1.4 times higher than that determined with Williams formula. Catchments 23 and 17 with a lower drainage length show comparatively higher values of soil loss in case of the Williams equation, which can be attributed to the greater importance of drainage length in the equation.

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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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Spatial knowledge databases as applied to the detection of changes in urban land use

Cited by 20 publications

References 9 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

Soil loss estimation through MUSLE using Kirpich and Williams times of concentration using RS and GIS techniques: a case study

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

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