Validation of Land Cover Products Using Reliability  Evaluation Methods

Shi, Wenzhong; Zhang, Xiaokang; Hao, Ming; Shao, Pan; Cai, Liping A.; Lyu, Xuzhe

doi:10.3390/rs70607846

Cited by 10 publications

(8 citation statements)

References 27 publications

(30 reference statements)

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“…To verify the effectiveness of the proposed approach, the CD results were evaluated by the following four widely used indices: 1) false alarm (FA) rate, 2) missed detection (MD) rate, 3) total error (TE) rate, and 4) Kappa coefficient [62][63][64].…”

Section: Evaluation Criteria and Experimental Settingsmentioning

confidence: 99%

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

Zhang

Shi

Lv³

et al. 2019

Remote Sensing

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This paper presents a novel approach for automatically detecting land cover changes from multitemporal high-resolution remote sensing images in the deep feature space. This is accomplished by using multitemporal deep feature collaborative learning and a semi-supervised Chan–Vese (SCV) model. The multitemporal deep feature collaborative learning model is developed to obtain the multitemporal deep feature representations in the same high-level feature space and to improve the separability between changed and unchanged patterns. The deep difference feature map at the object-level is then extracted through a feature similarity measure. Based on the deep difference feature map, the SCV model is proposed to detect changes in which labeled patterns automatically derived from uncertainty analysis are integrated into the energy functional to efficiently drive the contour towards accurate boundaries of changed objects. The experimental results obtained on the four data sets acquired by different high-resolution sensors corroborate the effectiveness of the proposed approach.

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Section: Evaluation Criteria and Experimental Settingsmentioning

confidence: 99%

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

Zhang

Shi

Lv³

et al. 2019

Remote Sensing

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“…Generally, the boundary of the extracted object cannot be strictly the same as that of the reference due to the error propagation during image interpretation. This phenomenon reduces the feasibility of using Equation (15). To improve the flexibility of B D , a tolerance is set to judge if the two objects are identical.…”

Section: Distance-based Accuracy Measuresmentioning

confidence: 99%

“…Noise is inherent in satellite images, and thus, the accuracy of object extraction needs to be examined. This issue has received considerable critical attention [8][9][10][11][12][13][14][15][16][17]. Examples include the error matrix and confusion matrix, which are two typical methods for accuracy assessment.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment Measures for Object Extraction from Remote Sensing Images

et al. 2018

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Object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm.

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“…However, the amount of uncertainty in existing LUC mapping information significantly impairs the reliability of classification products [5]. Researchers developing methods of measuring the uncertainty in LUC data focused on quantifying the uncertainty in remote sensing images, determining the classification uncertainty, and assessing the accuracy of LUC products [6][7][8][9][10][11][12]. For example, Griffith and Chun [7] studied the uncertainty in spatial autocorrelation parameters in a spatial autoregressive model associated with remotely sensed images.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity

Zhang

Shi

Lv³

2019

Remote Sensing

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Land use/cover (LUC) data are commonly relied on to provide land surface information in a variety of applications. However, the exchange and joint use of LUC information from different datasets can be challenging due to semantic differences between common classification systems (CSs). In this paper, we propose an uncertainty assessment schema to capture the semantic translation uncertainty between heterogeneous LUC CSs and evaluate the data label uncertainty of multitemporal LUC mapping results caused by uncertainty propagation. The semantic translation uncertainty between CSs is investigated using a dynamic semantic reference system (DSRS) model and semantic similarity analysis. An object-based unsupervised change detection algorithm is adopted to determine the probability of changes in land patches, and novel uncertainty metrics are proposed to estimate the patch label uncertainty in LUC maps. The proposed uncertainty assessment schema was validated via experiments on four LUC datasets, and the results confirmed that semantic uncertainty had great impact on data reliability and that the uncertainty metrics could be used in the development of uncertainty controls in multitemporal LUC mapping by referring to uncertainty assessment results. We anticipate our findings will be used to improve the applicability and interoperability of LUC data products.

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Validation of Land Cover Products Using Reliability Evaluation Methods

Cited by 10 publications

References 27 publications

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

Accuracy Assessment Measures for Object Extraction from Remote Sensing Images

Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity

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