Probabilistic Label Relaxation Research Articles

A Novel Unsupervised Object-Level Crop Rotation Detection With Time-Series Dual-Polarimetric SAR Data

Crop rotation is subsidized by the government because of its many advantages. Monitoring whether crop rotation is beneficial for agricultural management, and can also provide a reference for government subsidy policies for crop rotation. In this paper, we propose an unsupervised object-oriented crop rotation detection method using time-series polarimetric SAR (PolSAR) data. On the one hand, we construct the change detection matrix based on the likelihood ratio test (LRT) distance to perform temporal filtering. Then, the pixel-level temporal change image is generated using Shannon entropy and maximum between-class variance (OTSU). On the other hand, we perform temporal segmentation on time-series PolSAR images to obtain superpixel results. Finally, the object-level crop rotation results are obtained with the probabilistic label relaxation (PLR) model. 42 Sentinel-1 dual-polarization SAR datasets during 2018 and 2019 are selected for detecting crop rotation changes on farms within Jinchang, China. Experimental results show that the crop rotation detection accuracy and <i>Kappa</i> coefficients of this method can reach 96.21% and 0.8989, respectively.

Urban Impervious Surface Extraction Based on Multi-Features and Random Forest

Impervious surface data has become an indicator of the degree of urbanization and the environmental quality, which inspires the widely use of remote sensing technology in extracting impervious surface. In order to reduce the confusion between impervious surface and other landcover types, this paper proposed an effective urban impervious surface extraction method based on multi-features and Random Forest (RF). First, Sentinel-2 multispectral data and Luojia 1–01 images are employed to extracted multi-features, including spectral features, texture features and temporal features. Then, a feature selection method with null importance is proposed to remove irrelevant features. Finally, Probabilistic Label Relaxation (PLR) is introduced into RF to obtain the classification results and impervious surface. The main urban areas of Zhengzhou and Hangzhou are selected as study areas. The experiment results show that the integration of the multi-features can significantly improve the overall accuracy of classification and the extraction accuracy of impervious surface. And the classification accuracy can be further improved after the feature selection with null importance. Besides, the PLR method can effectively reduce the salt-and-pepper phenomenon of classification results using random forest, which presupposes the optimal number of iterations. The method proposed in this paper can effectively improve the estimation of impervious surface and provide an important reference for the extraction of impervious surface based on pixel level.

Improving LiDAR classification accuracy by contextual label smoothing in post-processing

We propose a contextual label-smoothing method to improve the LiDAR classification accuracy in a post-processing step. Under the framework of global graph-structured regularization, we enhance the effectiveness of label smoothing from two aspects. First, each point can collect sufficient label-relevant neighborhood information to verify its label based on an optimal graph. Second, the input label probability set is improved by probabilistic label relaxation to be more consistent with the spatial context. With this optimal graph and reliable label probability set, the final labels are computed by graph-structured regularization. We demonstrate the contextual label-smoothing approach on two separate urban airborne LiDAR datasets with complex urban scenes. Significant improvements in the classification accuracies are achieved without losing small objects (such as façades and cars). The overall accuracy is increased by 7.01% on the Vienna dataset and 6.88% on the Vaihingen dataset. Moreover, most large, wrongly labeled regions are corrected by long-range interactions that are derived from the optimal graph, and misclassified regions that lack neighborhood communications in terms of correct labels are also corrected with the probabilistic label relaxation.

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. A novel supervised superpixel-based classification method is proposed in this study to suppress the influence of speckle noise on PolSAR images for the purpose of obtaining accurate and consistent classification results. This method combines statistical information with spatial context information based on the stochastic expectation maximization (SEM) algorithm. First, a modified simple linear iterative clustering (SLIC) algorithm is utilized to generate superpixels as classification elements. Second, class posterior probabilities of superpixels are calculated by a K distribution in iterations of SEM. Then, a neighborhood function is defined to express the spatial relationship among adjacent superpixels quantitatively, and the class posterior probabilities are updated by this predefined neighborhood function in a probabilistic label relaxation (PLR) procedure. The final classification result is obtained by the maximum a posteriori decision rule. A simulated image, a spaceborne RADARSAT-2 image, and an airborne AIRSAR image are used to evaluate the proposed method, and the classification accuracy of our proposed method is 99.28%, 93.16% and 89.70%, respectively. The experimental results indicate that the proposed method obtains more accurate and consistent results than other methods.

A new Likelihood Ratio for supervised classification of fully polarimetric SAR data: An application for sea ice type mapping

One of the potential applications of polarimetric Synthetic Aperture Radar (SAR) data is the classification of land cover, such as forest canopies, vegetation, sea ice types, and urban areas. In contrast to single or dual polarized SAR systems, full polarimetric SAR systems provide more information about the physical and geometrical properties of the imaged area. This paper proposes a new Bayes risk function which can be minimized to obtain a Likelihood Ratio (LR) for the supervised classification of polarimetric SAR data. The derived Bayes risk function is based on the complex Wishart distribution. Furthermore, a new spatial criterion is incorporated with the LR classification process to produce more homogeneous classes. The application for Arctic sea ice mapping shows that the LR and the proposed spatial criterion are able to provide promising classification results. Comparison with classification results based on the Wishart classifier, the Wishart Likelihood Ratio Test Statistic (WLRTS) proposed by Conradsen et al. (2003) and the Expectation Maximization with Probabilistic Label Relaxation (EMPLR) algorithm are presented. High overall classification accuracy of selected study areas which reaches 97.8% using the LR is obtained. Combining the derived spatial criterion with the LR can improve the overall classification accuracy to reach 99.9%. In this study, fully polarimetric C-band RADARSAT-2 data collected over Franklin Bay, Canadian Arctic, is used.

A Spatial Contextual Postclassification Method for Preserving Linear Objects in Multispectral Imagery

Classification of remote sensing multispectral data is important for segmenting images and thematic mapping and is generally the first step in feature extraction. Per-pixel classification, based on spectral information alone, generally produces noisy classification results. The introduction of spatial information has been shown to be beneficial in removing most of this noise. Probabilistic label relaxation (PLR) has proved to be advantageous using second-order statistics; here, we present a modified contextual probabilistic relaxation method based on imposing directional information in the joint probability with third-order statistics. The proposed method was tested in synthetic images and real images; the results are compared with a “Majority” algorithm and the classical PLR method. The proposed third-order method gives the best results, both visually and numerically.

Visualization and unsupervised classification of changes in multispectral satellite imagery

The statistical techniques of multivariate alteration detection, minimum/maximum autocorrelation factors transformation, expectation maximization and probabilistic label relaxation are combined in a unified scheme to visualize and to classify changes in multispectral satellite data. The methods are demonstrated with an example involving bitemporal LANDSAT TM imagery.

A means for utilizing ancillary information in multispectral classification

A method is presented that allows information from ancillary data sources to be incorporated into the results of an existing classification of remotely sensed data. Based upon probabilistic label relaxation procedures, which are used for imbedding spatial context data in image-labeling problems, the method utilizes the source of ancillary information in the form of a set of probabilities. These are injected into a modified relaxation method called supervised relaxation labeling which, on application, develops a labeling for remotely sensed data that strikes a balance in consistency between spectral, spatial, and ancillary data sources of information. Results are presented of a forestry classification in which accuracy is improved from 68% to 81% by incorporating topographic elevation in the manner outlined.