Application of Decision Tree in Land Use Classification

Abstract— In this paper, Landsat ETM+ image of Huainan city in Anhui were classified with a decision tree, which was established based on the analysis of the spectrum characteristics, the texture characteristics and other auxiliary information, such as NDVI, NDBI and topography characteristics. Then the author compared decision tree classification technology with maximum likelihood classification method. The result indicated that the accuracy of decision tree classification was 4.06% higher than that of the maximum likelihood classification and Kappa coefficient was increased by 5.61%. These show that decision tree classification technology is flexible and can improve the classification accuracy efficiently. Keywords— remote sensing; decision tree classification; texture characteristics; information extraction
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variance of objects. Figure 1 is the spectrum characteristic of representative features.
to distinguish most of the vegetation. NDVI is calculated as follows: NIR RED B(4) B(3) (2) NDVI NIR RED B(4) B(3) Normalized difference barren index is an index reflecting architecture of information. It reveals the characteristics of the surface exposed, and it can be used to distinguish construction land [6]. NDBI is calculated as follows: B(5) B(4) (3) NDBI B(5) B(4) SRTM provide information of topography elevation. According to topographical distribution of study area, we can distinguish part of forest and grass and farm land using SRTM. D. Construction of decision tree classifier By analyzing Spectrum characteristic of representative features, texture features and auxiliary information, a decision tree classifier for study area had constructed. Figure 2 is the classification process. The threshold of each node had been determined after a large number of experiments : k1 = 25, K2 = 0.1, k3 = 16 and k4 = 1.5.
II.
DAT A SOURCES AND RESEARCH METHODS
A. Data Sources In the paper, we selected Landsat ETM+, Spot-5, and SRTM images of Huainan city as the data sources. Remote sensing data and parameters selected shown in table I. Study area was located in north central Anhui Province. The topography of study area was divided into two parts by Huaihe River, hills in south of Huaihe River and flat-lying plain in north.
A. Spectral information extraction We selected a large number of sample points for each object, measured spectrum values of each point using ENVI4.5, and calculated maximu m, minimu m, mean and
Spot-5 Landsat ETM+ SRTM Vector graph
B. Research methods and processes The purpose of this paper is to test and compare methods of classification. According to the study area characteristics and land use classification manner in remote sensing survey of land resources, study area was divided into 5 objects: water body, construction land, farmland, forest and grass and unused land. Based on Spot-5 image, the Landsat ETM+ image were regulated with the quadratic polynomial, and the deviation is controlled in 0.5 pixels; the Landsat ETM+ image were clipped by vector graph of study area; spectral characteristics, texture characteristics, topography characteristics and other auxiliary information were extracted and analyzed, the nodes and thresholds were determined; a decision tree classifier was established; used this decision tree classifier in study area, calculated the classification accuracy and compared result with maximu m likelihood classification. III. DECISION T REE CLASSIFICAT ION
I.
INT RODUCTION
The basic principles of remote sensing image classification is that remote sensing image will be classified different categories by some rules or algorith m based on spectral brightness, spatial structure and other information of each pixel in different bands [1]. Traditional supervised classification and unsupervised classification are based mainly on the spectral features, and remote sensing has the phenomenon of “the same object with different spectral” and “different object with the same spectral”, so the results of these methods are lo wer classification accuracy [2]. To solve these problems, in recent years, a number of new classification algorithms appeared, including BP neural network, support vector machines (SVM), mu lti-classifier and decision tree, etc. These algorithms take various kinds of information (spectral features, texture characteristics and topographic information, etc.) into the classification system to improve the classification accuracy, then will provide more reliable basis for land use study. Based on these, in this paper, Spectral characteristics, texture characteristics, topography characteristics and other auxiliary informat ion were acquired and analyzed, the Landsat ETM+ image of Huainan city in 2000 were classified using decision tree classification, then the result of decision tree classification were co mpared and analyzed with that of maximu m likelihood classification.
Wang Qing3 ; Lian Dajun4 ;Wang Zhijie5
3: School of Instrument Science and Engineering, Southeast University 4: Su zhou University of Science and Technology 5: Nan jing Fo restry University Suzhou, China
Application of Decision Tree in Land Use Classification
Wang Wei1,2 ; Wang Yunjia2 ;
1: Research Institute of Southeast University in Su zhou 2: College of Environment and Spatial In formatics, China University of M ining and Technology Suzhou, China E-mail:wwangcu mt@163.co m
TABLE I. REMOTE SENSING DATA AND PARAMETERS
Number 1 2 3 4
Data type
Phase 2003-8-6 2000-7-21 2005-8-12 --------
Row/Path -------121/37 121/37 --------
Resolution 10m 30m 30m --------