Kazhong Deng

Monitoring Mining Subsidence Using A Combination of Phase-Stacking and Offset-Tracking Methods

An approach to study the mechanism of mining-induced subsidence, using a combination of phase-stacking and sub-pixel offset-tracking methods, is reported. In this method, land subsidence with a small deformation gradient was calculated using time-series differential interferometric synthetic aperture radar (D-InSAR) data, whereas areas with greater subsidence were calculated by a sub-pixel offset-tracking method. With this approach, time-series data for mining subsidence were derived in Yulin area using 11 TerraSAR-X (TSX) scenes from 13 December 2012 to 2 April 2013. The maximum mining subsidence and velocity values were 4.478 m and 40 mm/day, respectively, which were beyond the monitoring capabilities of D-InSAR and advanced InSAR. The results were compared with the GPS field survey data, and the root mean square errors (RMSE) of the results in the strike and dip directions were 0.16 m and 0.11 m, respectively. Four important results were obtained from the time-series subsidence in this mining area: (1) the mining-induced subsidence entered the residual deformation stage within about 44 days; (2) the advance angle of influence changed from 75.6° to 80.7°; (3) the prediction parameters of mining subsidence; (4) three-dimensional deformation. This method could be used to predict the occurrence of mining accidents and to help in the restoration of the ecological environment after mining activities have ended.

Unsupervised change detection using fuzzy c-means and MRF from remotely sensed images

In this paper, a novel change detection approach is proposed using fuzzy c-means (FCM) and Markov random field (MRF). First, the initial change map and cluster (changed and unchanged) membership probability are generated through applying FCM to the difference image created by change vector analysis (CVA) method. Then, to reduce the over-smooth results in the traditional MRF, the spatial attraction model is integrated into the MRF to refine the initial change map. The adaptive weight is computed based on the cluster membership and distances between the centre pixel and its neighbourhood pixels instead of the equivalent value of the traditional MRF using the spatial attraction model. Finally, the refined change map is produced through the improved MRF model. Two experiments were carried and compared with some state-of-the-art unsupervised change detection methods to evaluate the effectiveness of the proposed approach. Experimental results indicate that FCMMRF obtains the highest accuracy among methods used in this paper, which confirms its effectiveness to change detection.

An improved pixel-tracking method for monitoring mining subsidence

ABSTRACT Underground mining always induces large vertical displacements in the ground surface and, because of the large subsidence gradient, the phase-unwrapping methods of interferometric synthetic aperture radar (InSAR) are unable to give accurate results. Pixel tracking based on normalized cross-correlation maximization overcomes the limitations imposed by the subsidence gradient and can be used to monitor large displacements. This paper introduces and analyses each component of the offset field and then describes the improved method used to efficiently remove orbital error and topography-related offset caused by rugged terrain with the help of a digital elevation model (DEM). Eleven TerraSAR-X images in spotlight mode and Shuttle Radar Tomography Mission (SRTM) DEM data are used to monitor mining subsidence by the improved pixel-tracking method in Shenmu County of Yulin City. The root mean square error (RMSE) between the improved method and Global Positioning System (GPS) data in the strike and dip directions are 0.143 and 0.108 m, respectively. The approach presented here is shown to be appropriate for monitoring large vertical displacements in mining subsidence.

A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images

Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to increase CD accuracy. First, two temporal images are stacked and segmented into multiscale segmentation maps. Then, a pixel-based change map with memberships belonging to changed and unchanged parts is obtained by fuzzy c-means clustering. Finally, based on the Dempster-Shafer evidence theory, the proposed scale-driven CD method incorporating uncertainty analysis is performed on the multiscale segmentation maps and the pixel-based change map. Two experiments were carried out on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and SPOT 5 data sets. The ratio of total errors can be reduced to 4.0% and 7.5% for the ETM+ and SPOT 5 data sets in this study, respectively. Moreover, the proposed approach outperforms some state-of-the-art CD methods and provides an effective solution for CD.

Fusion-based approach to change detection to reduce the effect of the trade-off parameter in the active contour model

This study proposes an approach to unsupervised change detection in which two different change maps are fused using different trade-off parameters of an active contour model. First, the change vector analysis method is conducted to produce a difference image from multitemporal and multispectral remotely sensed images. Second, two change maps are obtained based on the difference image using an active contour model using two different values of the trade-off parameter. Finally, an advantage fusion strategy is proposed to yield a final change map by fusing the two change maps, thereby reducing false alarms and preserving the accurate outlines of the changed regions. Two experiments are conducted with Landsat-7 Enhanced Thematic Mapper Plus and Landsat-5 Thematic Mapper data sets to evaluate the performance of the proposed method. Results confirm the effectiveness of the proposed approach vis-à-vis some of the state-of-the-art methods. This work contributes to the reduction of the effect of the trade-off parameter on the accuracy of the change map.

Large deformation monitoring over a coal mining region using pixel-tracking method with high-resolution Radarsat-2 imagery

ABSTRACT Differential synthetic aperture radar interferometry (D-InSAR) is limited when exploited in high-intensity mining areas, because large deformation gradients lie beyond the maximum measurable value of the D-InSAR technique which breaks the prerequisite for successfully employing of the method. The SAR amplitude-based pixel-tracking method provides an alternative way to efficiently and robustly extract the large deformation distribution particularly when the D-InSAR technique is limited by loss of coherence. In addition, the deformation in the line-of-sight direction and the deformation along the azimuth direction are also presented in this paper with 24-day interval repeat-pass high-resolution Rardarsat-2 imagery. Combining both of these techniques can help to better understand the deformation mechanisms associated with underground mining activities. The accuracies of 0.12 m in slant-range direction and 0.19 m in the azimuth direction were achieved, respectively. Besides, the profiles across the maximum deformation region have verified that the deformation occurred during two acquisition periods is far beyond the theoretical maximum deformation gradient corresponding to high-resolution C-band SAR data. The obtained surface motion infers to the mining activities and assessed damage caused by the large deformation.

Deriving Ice Motion Patterns in Mountainous Regions by Integrating the Intensity-Based Pixel-Tracking and Phase-Based D-InSAR and MAI Approaches: A Case Study of the Chongce Glacier

As a sensitive indicator of climate change, mountain glacier dynamics are of great concern, but the ice motion pattern of an entire glacier surface cannot be accurately and efficiently generated by the use of only phase-based or intensity-based methods with synthetic aperture radar (SAR) imagery. To derive the ice movement of the whole glacier surface with a high accuracy, an integrated approach combining differential interferometric SAR (D-InSAR), multi-aperture interferometry (MAI), and a pixel-tracking (PT) method is proposed, which could fully exploit the phase and intensity information recorded by the SAR sensor. The Chongce Glacier surface flow field is estimated with the proposed integrated approach. Compared with the traditional SAR-based methods, the proposed approach can determine the ice motion over a widely varying range of ice velocities with a relatively high accuracy. Its capability is proved by the detailed ice displacement pattern with the average accuracy of 0.2 m covering the entire Chongce Glacier surface, which shows a maximum ice movement of 4.9 m over 46 days. Furthermore, it is shown that the ice is in a quiescent state in the downstream part of the glacier. Therefore, the integrated approach presented in this paper could present us with a novel way to comprehensively and accurately understand glacier dynamics by overcoming the incoherence phenomenon, and has great potential for glaciology study.

A contrast-sensitive Potts model custom-designed for change detection

Abstract A contrast-sensitive Potts model (CSP) custom-designed for change detection is presented using remotely sensed images. In traditional Potts model, a constant penalty coefficient is used, which results in ignorance of significant details and excessively homogenous patches during change detection using the difference image generated from multitemporal images. In the proposed CSP, the difference image is divided into unchanged, uncertainty and changed regions. Then different linear functions are introduced instead of the constant penalty coefficient for different regions. Two experiments were carried on optical satellite images, and the results indicate that the proposed CSP produces more accurate change maps than some state-of-the-art methods.

An improved neighborhood-based ratio approach for change detection in SAR images

ABSTRACT The speckle noise of synthetic aperture radar (SAR) images limits its application in change detection. Compared with improved ratio (IR) and log-ratio (LR) operators, the neighborhood-based ratio (NR) technique can restrain the influence of speckle noise and is more suitable for change detection in SAR images. However, we find three drawbacks of NR by analyzing this method carefully. To overcome these defects, we propose an improved neighborhood-based ratio (INR) approach for change detection in SAR images. INR restructures the NR operator to exploit the neighborhood information more reasonably and is expected to reduce the impact of speckle noise better. IR, LR, mean ratio operator, NR, and INR are tested on two data sets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can obtain better difference image than other state-of-art methods and improve the accuracy of change detection in SAR images effectively.

Change detection in multispectral images based on multiband structural information

ABSTRACT Change vector analysis (CVA) and spectral angle mapper (SAM) are usually used to generate difference image in change detection of multispectral images. Although CVA and SAM can describe the difference between multispectral images, they are defined mathematically and lack support of human visual system (HVS) theory. Advanced structural similarity (ASSIM) complies with the pattern that human perceives the changes occurred in an objective scene. Nevertheless, ASSIM was designed for single band images and cannot be used for extracting multiband structural information from multispectral images directly. Therefore, we first propose two strategies to extract multiband structural information from multiband images. Then, we propose the approaches based on multiband structural information for change detection in multispectral images. Experimental results from one semisynthetic data set and two real data sets acquired by Sentinel-2A and QuickBird satellites validate the effectiveness of the proposed approaches.