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|Table of Contents|

基于易康软件的QuickBird遥感影像林分类型识别——以福建省将乐林场为例(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2019年01期
Page:
127-134
Column:
研究论文
publishdate:
2019-01-28

Article Info:/Info

Title:
Forest stand identification based on eCognition software using QuickBird remote sensing image: a case of Jiangle Forest Farm in Fujian Province
Article ID:
1000-2006(2019)01-0127-08
Author(s):
MAO XuegangYAO Yao CHEN Shuxin LIU Jiaqian DU Zihan WEI Jingyu
College of Forestry, Northeast Forestry University, Harbin 150040, China
Keywords:
forest stand identification high spatial resolution scale segmentation object-oriented classification support vector muchine(SVM) Jiangle Forest Farm Fujian Province
Classification number :
S757
DOI:
10.3969/j.issn.1000-2006.201708004
Document Code:
A
Abstract:
【Objective】 Identification of forest stand is critical for forest resources monitoring. 【Method】To study the extraction of forest stand information based on object oriented method, QuickBird remote sensing image with multispectral bands(blue, green, red and near-infrared )was used as the experimental data, and 10 segmentation scales(25-250, step size 25)were carried out using eCognition Developer 8.7. For each segmentation scale, the support vector machine with linear kernel was applied to three combination features(spectrum, spectrum+ texture, spectrum+ texture+ space), respectively. 【Result】The results showed that segmentation scale was significant to forest stand identification, with a highest segmentation quality at segmentation scale of 150. At each of 10 segmentation scales, introducing texture features into spectral features could improve accuracy of classification; however, introducing spatial features into spectral features had no influence on accuracy of classification. So the highest accuracy of classification(OA=85%; Kappa value is 0.86)was obtained based on the integration of spectral and texture features at segmentation scale of 150. 【Conclusion】Segmentation scale plays an important role in tree species classification.At all scales(25-250), overall accuracy of spectral and texture features was higher than that of overall accuracy using spectral features alone. Spatial features did not play a role in forest classification. Matches between segmented and reference objects produced higher classification accurate, and slight over- and under-segmentations did not significantly affect the classifications. The object-based method based on eCognition software can obtain satisfactory results for classification of stand types from QuickBird multi-band remote sensing data.

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Last Update: 2019-01-28