Subset image classification maps for the Bole dataset.

<p>Subset image classification maps for the Bole dataset.</p

Scatter plots of NDVI data from HJ-1 CCD andLandsat-5 TM images.

<p>Note: The red lines are the 1:1 lines, and the dashed lines are fitted lines of HJ-1 NDVI and Landsat-5 NDVI.</p

Methodology of the study.

<p>Methodology of the study.</p

Summary of variables and parameters used in the segmentation.

<p>Summary of variables and parameters used in the segmentation.</p

McNemar’s test for Manas.

<p>Notes: The M-voting and P-fusion are implemented at both the pixel level and object level. N = No significance, S+ = Positive significance, S- = Negative significance. Classifiers in column are classifiers 1 and classifiers in line are classifiers 2 of section 3.3.</p><p>McNemar’s test for Manas.</p

Study areas: (a) Location of Bole and Manas in Xinjiang and extent of Xinjiang. (b) Bole (2011/7/11) and (c) Manas (2011/7/13).

<p>The red patterns on the images are distributions of ground reference data. </p

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

<div><p>A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern.</p></div

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<p>Class-specific producer’s accuracies (PA), user’s accuracies (UA), and overall accuracies (OA) (%) for the different classifiers (Bole, training sample number = 4,000).</p

Overall accuracy of different classifiers using different training sample sets (Manas).

<p>Note: The error bars are the standard deviation of the overall accuracy.</p

Number of training and validation samples.

<p>Number of training and validation samples.</p