Abstract
Texture provides spatial features complementary to spectral information in land cover classification of high spatial resolution imagery. In texture classification, window size is an important factor influencing classification accuracy, but selecting the optimal window size is difficult. In this paper, we propose an optimized window size texture classification method which can solve the window size selection problem. In order to validate the new method, we designed four classification experiments with different input features based on SPOT-5 imagery: (1) spectral features, (2) spectral features and single window size texture features, (3) spectral features and multiple window size texture features and (4) spectral features and optimized window size texture features based on posterior probabilities. Overall, the highest accuracy was obtained using the optimized window size texture classification, which does not require window size selection before classification. Furthermore, the results imply that optimized window size varies with land cover type.
| Original language | English |
|---|---|
| Pages (from-to) | 753-762 |
| Number of pages | 10 |
| Journal | Remote Sensing Letters |
| Volume | 5 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 3 Aug 2014 |
| MoE publication type | A1 Journal article-refereed |
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Posterior probability-based optimization of texture window size for image classification. / Liu, Jinxiu; Liu, Huiping; Heiskanen, Janne; Mõttus, Matti; Pellikka, Petri.
In: Remote Sensing Letters, Vol. 5, No. 8, 03.08.2014, p. 753-762.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Posterior probability-based optimization of texture window size for image classification
AU - Liu, Jinxiu
AU - Liu, Huiping
AU - Heiskanen, Janne
AU - Mõttus, Matti
AU - Pellikka, Petri
PY - 2014/8/3
Y1 - 2014/8/3
N2 - Texture provides spatial features complementary to spectral information in land cover classification of high spatial resolution imagery. In texture classification, window size is an important factor influencing classification accuracy, but selecting the optimal window size is difficult. In this paper, we propose an optimized window size texture classification method which can solve the window size selection problem. In order to validate the new method, we designed four classification experiments with different input features based on SPOT-5 imagery: (1) spectral features, (2) spectral features and single window size texture features, (3) spectral features and multiple window size texture features and (4) spectral features and optimized window size texture features based on posterior probabilities. Overall, the highest accuracy was obtained using the optimized window size texture classification, which does not require window size selection before classification. Furthermore, the results imply that optimized window size varies with land cover type.
AB - Texture provides spatial features complementary to spectral information in land cover classification of high spatial resolution imagery. In texture classification, window size is an important factor influencing classification accuracy, but selecting the optimal window size is difficult. In this paper, we propose an optimized window size texture classification method which can solve the window size selection problem. In order to validate the new method, we designed four classification experiments with different input features based on SPOT-5 imagery: (1) spectral features, (2) spectral features and single window size texture features, (3) spectral features and multiple window size texture features and (4) spectral features and optimized window size texture features based on posterior probabilities. Overall, the highest accuracy was obtained using the optimized window size texture classification, which does not require window size selection before classification. Furthermore, the results imply that optimized window size varies with land cover type.
UR - http://www.scopus.com/inward/record.url?scp=84907879664&partnerID=8YFLogxK
U2 - 10.1080/2150704X.2014.963895
DO - 10.1080/2150704X.2014.963895
M3 - Article
AN - SCOPUS:84907879664
VL - 5
SP - 753
EP - 762
JO - Remote Sensing Letters
JF - Remote Sensing Letters
SN - 2150-704X
IS - 8
ER -