scholarly journals OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

2020 ◽  
Vol 12 (6) ◽  
pp. 987 ◽  
Author(s):  
Guangbin Lei ◽  
Ainong Li ◽  
Jinhu Bian ◽  
He Yan ◽  
Lulu Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Classifier Ensemble ◽  
Land Cover Mapping ◽  
Initial Training ◽  
Sample Collection ◽  
Training Samples ◽  
Multiple Classifier ◽  
Practical Object

Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%–8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.

A Novel Unsupervised Sample Collection Method for Urban Land-Cover Mapping Using Landsat Imagery

2019 ◽  
Vol 57 (6) ◽  
pp. 3933-3951 ◽  
Author(s):  
Jiayi Li ◽  
Xin Huang ◽  
Ting Hu ◽  
Xiuping Jia ◽  
Jon Atli Benediktsson
Keyword(s):  
Land Cover ◽  
Landsat Imagery ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Sample Collection ◽  
Collection Method ◽  
Urban Land Cover

scholarly journals Application of Full Polarimetric ALOS PALSAR for Land Cover Mapping In Sleman District

PROMINE ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 33-40
Author(s):  
Like Indrawati
Keyword(s):  
Land Cover ◽  
Research Area ◽  
Rice Fields ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Alos Palsar ◽  
Accuracy Test ◽  
Open Land ◽  
Land Cover Maps

The simplest way to interpret polarimetric imagery for land cover classification is to use visualinterpretation methods. The existence of interpretations key as a tool for visual interpretation becomesimportant when different interpreters can produce different results. The quality of the results of theinterpretation of land cover is then determined by the quality of the interpretation tool, in this case, thekey to the interpretation of land cover. The purpose of this study was to make the key to land coverclass interpretation in the Full Polarimetric ALOS PALSAR image, then the interpretation key wasused for reference in making land cover maps and measuring the accuracy of the results of the visualinterpretation. The image used in this study consisted of HH, VV, HV and VH bands. The location ofthe study was in parts of Sleman District. The analysis is done visually by on-screen digitizing onALOS Palsar composite HH + VV HV + VH HH-HV image, which is then interpreted key. The truetest is done by means of the overall accuracy test and Kappa. Visually, ALOS PALSAR imagery isable to distinguish 12 land cover classes in the research area, namely built land, rice fields, mixedgardens, moorlands, salak garden, grass, forest, shrubs, open land, airports, water bodies and lavawith 83% Overall accuracy, and 78% Kappa accuracy.

Object‐oriented classification for urban land cover mapping with ASTER imagery

2007 ◽  
Vol 28 (20) ◽  
pp. 4645-4651 ◽  
Author(s):  
Y. Chen ◽  
P. Shi ◽  
T. Fung ◽  
J. Wang ◽  
X. Li
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Urban Land Cover

scholarly journals Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin

2021 ◽  
Vol 13 (8) ◽  
pp. 1596
Author(s):  
Bo Zhong ◽  
Aixia Yang ◽  
Kunsheng Jue ◽  
Junjun Wu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Regional Scale ◽  
Heihe River ◽  
Land Cover Mapping ◽  
High Quality ◽  
Long Time Series ◽  
Training Samples ◽  
Long Time ◽  
Land Cover Map

Long time series of land cover changes (LCCs) are critical in the analysis of long-term climate, environmental, and ecological changes. Although several moderate to fine resolution global land cover datasets have been publicly released and they show strong consistency at the global scale, they have large deviations at the regional scale; furthermore, high-quality land cover datasets from before 2000 are not available and the classification consistency among different datasets is not very good. Thus, long time series of land cover datasets with high quality and consistency are in great demand but they are still unavailable, even at the regional scale. The Landsat series of satellite imagery composed of eight successive satellites can be traced back to 1972 and it is, therefore, possible to produce a long time series land cover dataset. In addition, the newly available satellite data have the capability to construct time series satellite images and a time series analysis method such as LCMM can be employed for making high-quality land cover datasets. Therefore, by taking the advantages of the two categories of satellite data, we proposed a new time series land cover mapping method based on machine learning and it, thereafter, is applied to Heihe River Basin (HRB) for verification purposes. Firstly, the high-quality land cover datasets at HRB from 2011–2015, which were retrieved using the LCMM method, are used for quickly and accurately making training samples. Secondly, a strategy for transferring the training samples after 2011 to earlier years is established. Thirdly, the random forest model is employed to train the selected yearly samples and a land cover map for every year is subsequently made. Finally, comprehensive analysis and validation are carried out for evaluation. In this study, a long time series land cover dataset including 1986, 1990, 1995, 2000, 2005, 2010, 2011, 2012, 2013, 2014, and 2015 is finally made and an average precision of about 90% is achieved. It is the longest time series land cover map with 30 m resolution at HRB and the dataset has good time continuity and stability.

scholarly journals Improving the Accuracy of Land Cover Mapping by Distributing Training Samples

2021 ◽  
Vol 13 (22) ◽  
pp. 4594
Author(s):  
Chenxi Li ◽  
Zaiying Ma ◽  
Liuyue Wang ◽  
Weijian Yu ◽  
Donglin Tan ◽  
...  
Keyword(s):  
Land Cover ◽  
Object Oriented ◽  
Land Cover Classification ◽  
Training Sample ◽  
Stratified Sampling ◽  
Systematic Sampling ◽  
Sampling Strategies ◽  
Sample Distribution ◽  
Training Samples ◽  
Sampling Approach

High-quality training samples are essential for accurate land cover classification. Due to the difficulties in collecting a large number of training samples, it is of great significance to collect a high-quality sample dataset with a limited sample size but effective sample distribution. In this paper, we proposed an object-oriented sampling approach by segmenting image blocks expanded from systematically distributed seeds (object-oriented sampling approach) and carried out a rigorous comparison of seven sampling strategies, including random sampling, systematic sampling, stratified sampling (stratified sampling with the strata of land cover classes based on classification product, Latin hypercube sampling, and spatial Latin hypercube sampling), object-oriented sampling, and manual sampling, to explore the impact of training sample distribution on the accuracy of land cover classification when the samples are limited. Five study areas from different climate zones were selected along the China–Mongolia border. Our research identified the proposed object-oriented sampling approach as the first-choice sampling strategy in collecting training samples. This approach improved the diversity and completeness of the training sample set. Stratified sampling with strata defined by the combination of different attributes and stratified sampling with the strata of land cover classes had their limitations, and they performed well in specific situations when we have enough prior knowledge or high-accuracy product. Manual sampling was greatly influenced by the experience of interpreters. All these sampling strategies mentioned above outperformed random sampling and systematic sampling in this study. The results indicate that the sampling strategies of training datasets do have great impacts on the land cover classification accuracies when the sample size is limited. This paper will provide guidance for efficient training sample collection to increase classification accuracies.

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