Mingsheng Liao

Using SAR Images to Detect Ships From Sea Clutter

An innovative constant false alarm rate (CFAR) algorithm was studied for ship detection using synthetic aperture radar (SAR) images of the sea. Two advances were achieved. An alpha-stable distribution rather than a traditional Weibull or -distribution was used to model the distribution of sea clutter. The distribution of sea clutter in a SAR image was typically heterogeneous, caused mainly by variable wind and current conditions. Image segmentation was carried out to improve the homogeneity of the distribution in each subimage or region. In comparison with ship detection using the CFAR algorithms based on the Weibull or K -distribution, our algorithm detected the most number of ships with the smallest number of false alarms.

Building-damage detection using post-seismic high-resolution SAR satellite data

Radar satellite imagery was valuable in supporting extensive rescue operations after the Wenchuan Earthquake on 12 May 2008 due to its ability to operate independently of weather conditions, day and night. However, it is a challenging task to identify damaged or destroyed buildings using synthetic aperture radar (SAR) data. The standard procedure for identifying damaged buildings is to use change detection by comparing post-seismic to pre-seismic images, but almost no archived high-resolution SAR images were available of the rather remote area damaged by the Wenchuan Earthquake. Building-damage assessment using only post-event SAR images was therefore necessary to assess the areas of damage. In this paper, theoretical assumptions about the appearance of collapsed buildings in high-resolution SAR images were drawn and verified with visual feature interpretations of real SAR images from the area.

Ship Detection in SAR Image Based on the Alpha-stable Distribution

This paper describes an improved Constant False Alarm Rate (CFAR) ship detection algorithm in spaceborne synthetic aperture radar (SAR) image based on Alpha-stable distribution model. Typically, the CFAR algorithm uses the Gaussian distribution model to describe statistical characteristics of a SAR image background clutter. However, the Gaussian distribution is only valid for multilook SAR images when several radar looks are averaged. As sea clutter in SAR images shows spiky or heavy-tailed characteristics, the Gaussian distribution often fails to describe background sea clutter. In this study, we replace the Gaussian distribution with the Alpha-stable distribution, which is widely used in impulsive or spiky signal processing, to describe the background sea clutter in SAR images. In our proposed algorithm, an initial step for detecting possible ship targets is employed. Then, similar to the typical two-parameter CFAR algorithm, a local process is applied to the pixel identified as possible target. A RADARSAT-1 image is used to validate this Alpha-stable distribution based algorithm. Meanwhile, known ship location data during the time of RADARSAT-1 SAR image acquisition is used to validate ship detection results. Validation results show improvements of the new CFAR algorithm based on the Alpha-stable distribution over the CFAR algorithm based on the Gaussian distribution.

Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up‐to‐date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub‐pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS‐2 SAR data. Validated by reference data derived from high‐resolution colour‐infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of worlds cities located in cloud‐prone and rainy areas.

Quantifying Sub-pixel Urban Impervious Surface through Fusion of Optical and InSAR Imagery

In this study, we explored the potential to improve urban impervious surface modeling and mapping with the synergistic use of optical and Interferometric Synthetic Aperture Radar (InSAR) imagery. We used a Classification and Regression Tree (CART)-based approach to test the feasibility and accuracy of quantifying Impervious Surface Percentage (ISP) using four spectral bands of SPOT 5 high-resolution geometric (HRG) imagery and three parameters derived from the European Remote Sensing (ERS)-2 Single Look Complex (SLC) SAR image pair. Validated by an independent ISP reference dataset derived from the 33 cm-resolution digital aerial photographs, results show that the addition of InSAR data reduced the ISP modeling error rate from 15.5% to 12.9% and increased the correlation coefficient from 0.71 to 0.77. Spatially, the improvement is especially noted in areas of vacant land and bare ground, which were incorrectly mapped as urban impervious surfaces when using the optical remote sensing data. In addition, the accuracy of ISP prediction using InSAR images alone is only marginally less than that obtained by using SPOT imagery. The finding indicates the potential of using InSAR data for frequent monitoring of urban settings located in cloud-prone areas.

Landslide monitoring with high-resolution SAR data in the Three Gorges region

Employing the well-known D-InSAR technique, we investigated landslide monitoring in the Three Gorges region using TerraSAR-X data. The experiment demonstrates that using both the amplitude and differential phase allows us to identify the precise location, deformation and time range of occurrence of certain landslides. To overcome the atmospheric effect on D-InSAR results, a time-series analysis was also carried out. The observed nonlinear relationship between the deformation and water level suggests that reservoir water level fluctuation is one of the major causes of landslides, which is significant in terms of issuing landslide warnings. In addition, the comparison of TerraSAR-X and C-band ASAR data results indicates that TerraSAR-X data provide far more reasonable deformation measurements because of their high temporal and spatial resolutions.

Reconstruction of DEMs From ERS-1/2 Tandem Data in Mountainous Area Facilitated by SRTM Data

A new approach is presented in this paper to produce Digital Elevation Model (DEM) in mountainous areas with steep slope using ERS-1/2 tandem data. In order to reduce the impact of phase errors on the Interferometric Synthetic Aperture Radar (InSAR)-generated DEM, an external DEM such as that from Shuttle Radar Topography Mission (SRTM) is utilized in this approach. The proposed algorithm includes two steps: The first step is to model and remove phase trends with a linear regression analysis before converting phase to height; the second step is to filter unreliable height points before interpolating the DEM from the InSAR height map. The critical points are the following: 1) determining the one-to-one correspondence between the interferogram and the SRTM DEM before knowing the InSAR-derived elevation values and 2) estimating the elevation range of every pixel from SRTM DEM. To solve the first problem, an iteratively geocoding algorithm is performed. A DEM interpolation error model solves the second one. For InSAR data processing, the SRTM DEM is not only usable for modeling systematic phase errors but also for filtering gross height errors. The experiments in Zhangbei and the Three Gorges areas in China show that our approach has improved the accuracy of the resulting DEMs significantly without any ground control points.

Analysis of the water volume, length, total area and inundated area of the Three Gorges Reservoir, China using the SRTM DEM data

Using the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) data covering the region of the Three Gorges Reservoir, Changjiang, China, we have computed the water volume, length, and total and inundated areas of the reservoir, with the assumption that the water surface within the reservoir is flat. When the reservoirs surface water level is 175 m above the mean sea level, the computed values may be comparable to the official data published by the Chinese government.

Characterization of Landslide Deformations in Three Gorges Area Using Multiple InSAR Data Stacks

In the areas with steep topography and vulnerable geological condition, landslide deformation monitoring is an important task for risk assessment and management. Differential Synthetic-Aperture Radar interferometry (D-InSAR) and Persistent Scatterer Interferometry (PS-InSAR) are two advanced SAR Interferometry techniques for detection, analysis and monitoring of slow moving landslides. The techniques can be used to identify wide displacement areas and measure displacement rates over long time series with millimeter-level accuracy. In this paper, multiple SAR datasets of Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) and Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) are used for landslide monitoring with both D-InSAR and PS-InSAR techniques in Badong at the Three Gorges area in China. Two areas of significant deformation along the southern riverbank of Yangtze River in Badong are identified by joint analyses of PS-InSAR results from different data stacks. Furthermore, both qualitative and quantitative evaluations of the PS-InSAR results are carried out together with preliminary correlation analysis between the time series deformation of a PS point in high risk location and the temporal variation of water level in the Three Gorges Reservoir.

Texture Classification of PolSAR Data Based on Sparse Coding of Wavelet Polarization Textons

This paper presents a frame for classifying polarimetric synthetic aperture radar (PolSAR) data. The frame is based on the combination of wavelet polarization information, textons, and sparse coding. Polarimetric synthesis unites with the discrete wavelet frame to obtain wavelet polarization variance through the calculation of the wavelet variance in the space of polarization states. The K-means cluster algorithm is implemented to cluster the wavelet polarization variance vectors of the training samples for the purpose of constructing a texton dictionary. A patch, in which all the wavelet polarization variance vectors match those in the texton dictionary, is used to obtain a statistical histogram. Sparse coding is applied to describe the histogram feature and generate a new texture feature called sparse coding of a wavelet polarization texton. Finally, support vector machine is used for the classification. All experiments are carried out on five sets of PolSAR data. The experimental results confirm that the proposed method effectively classifies PolSAR data.