Hongga Li

A level set filter for speckle reduction in SAR images

Despite much effort and significant progress in recent years, speckle removal for Synthetic Aperture Radar (SAR) image still is a challenging problem in image processing. Unlike the traditional noise filters, which are mainly based on local neighborhood statistical average or frequencies transform, in this paper, we propose a speckle reduction method based on the theory of level set, one form of curvature flow propagation. Firstly, based on partial differential equation, the Lee filter can be cast as a formulation of anisotropic diffusion function; furthermore, we continued to deduce it into a level set formulation. Level set flow into the method allows the front interface to propagate naturally with topological changes, where the speed is proportional to the curvature of the intensity contours in an image. Hence, small speckle will disappear quickly, while large scale interfaces will be slow to evolve. Secondly, for preserving finer detailed structures in images when smoothing the speckle, the evolution is switched between minimum or maximum curvature speed depending on the scale of speckle. The proposed method has been illustrated by experiments on simulation image and ERS-2 SAR images under different circumstances. Its advantages over the traditional speckle reduction filter approaches have also been demonstrated.

A Fast Level Set Method for Synthetic Aperture Radar Ocean Image Segmentation

Segmentation of high noise imagery like Synthetic Aperture Radar (SAR) images is still one of the most challenging tasks in image processing. While level set, a novel approach based on the analysis of the motion of an interface, can be used to address this challenge, the cell-based iterations may make the process of image segmentation remarkably slow, especially for large-size images. For this reason fast level set algorithms such as narrow band and fast marching have been attempted. Built upon these, this paper presents an improved fast level set method for SAR ocean image segmentation. This competent method is dependent on both the intensity driven speed and curvature flow that result in a stable and smooth boundary. Notably, it is optimized to track moving interfaces for keeping up with the point-wise boundary propagation using a single list and a method of fast up-wind scheme iteration. The list facilitates efficient insertion and deletion of pixels on the propagation front. Meanwhile, the local up-wind scheme is used to update the motion of the curvature front instead of solving partial differential equations. Experiments have been carried out on extraction of surface slick features from ERS-2 SAR images to substantiate the efficacy of the proposed fast level set method.

Remote sensing analysis of the distribution and genetic mechanisms of transportation network damage caused by the Wenchuan earthquake

Transportation networks are among the most important lifelines for post-seismic relief and reconstruction. It is imperative to investigate, monitor, and analyze transportation network damage caused by earthquake disasters in near real-time. Herein, we present a method for the analysis of seismic hazards and the subsequent assessment of the impact of the Wenchuan earthquake on transportation networks employing remote sensing and geographical information systems. In this method, the locations, shapes, lengths, and areas of the main damaged segments of state and provincial highways are interpreted and surveyed based on airborne ADS40 data and diverse remotely sensed satellite images of varying resolutions before and after the disaster. Next, the spatial distributions of geological disasters such as landslides, land-collapses, mud-rock flows, bank-collapses, earthquake rifts, and faults, as well as barrier lakes, were analyzed. These types of geological disasters commonly cause transportation network blockage and damage. Finally, geographical factors, including geological structures, topography, and landscapes, were collected and integrated with the disaster statistics to quantitatively analyze the primary transportation seismic disaster indices, and evaluate the geographical characteristics and genetic mechanisms of seismic disasters. Our results indicate that transportation network blockage and damage occurred in 808 segments, with a total length of 170.2 km, and occupied 29.66% of the total length of the state and provincial highways in the core disaster regions. The distribution of transportation network blockage and damage has obvious geographical characteristics. It is concentrated in regions near geological faults, folds, rock crushes, and breaks, especially near the Longmenshan-controlling fault, which played a decisive role in the Wenchuan earthquake. The remotely sensed images, maps, and analytical results on the geographical distribution and genetic mechanisms of the transportation network blockage and damage effectively guided the national department of transportation repair and reconstruction planning for the disaster areas.

Optimizing expressway maintenance planning by coupling ant algorithm and geography information system transportation in Hubei province, China

Highway maintenance scheduling is a complex optimization problem and imposes a challenge for GIS-T research. In this paper, a new approach was put forward to determining the optimal set of alternatives for highway infrastructure facilities by using ant colony algorithm and GIS. In the proposed approach, GIS was used to analyze traffic flux, toll and maintenance time of each highway segment. Meanwhile, an ant colony algorithm was applied to resolve the computational complexity of multi-objective large-size scheduling optimization problems. Experiments were performed in national highways in the Hubei province by the proposed method. The application illustrated the feasibility, efficiency, and capability of coupling ant algorithm and geography information system transportation for multi-objective expressway maintenance planning optimization.

Prediction of urban land use evolution using temporal remote sensing data analysis and a spatial logistic model

Urban land use systems are complex systems with components, factors and agents from natural, environmental, social and economic systems. In this paper, we developed a remote sensing and GIS-based integrated approach to modeling and predicting spatially-explicit urban land use changes. The model was built using temporal remote sensing data land use analysis coupled with a Markov model and a spatial multinomial logistic regression framework. Experiments were performed in the Shenzhen Special Zone to substantiate the accuracy of the proposed method. We show that integration of a Markov model and a spatial logistic model is an effective method to describe urban land use evolution and meet the needs of land use early warning and annual land supply planning.

Remote sensing applications for petroleum resource exploration in offshore basins of China

In this paper, a new approach for detecting and analyzing sea surface slicks caused by hydrocarbon seepage of offshore petroleum accumulations has been developed. This approach uses remote sensing radar technology and geophysical exploration techniques and has been developed based on hydrocarbon seepage theory. In this study, Synthetic Aperture Radar (SAR) data were used as the main data source. These data were integrated with gravity data inversed from satellite altimeter data, geophysical abnormal data from airborne magnetic data, and geological data of oil-and gas-bearing basins. Using the geographical information system, the oil and gas accumulating areas were outlined by the prospect models. This approach for the exploration and evaluation for offshore petroleum accumulations has been applied to two study areas in offshore petroleum basins in China: the Bohai Sea and Pearl River Mouth basins. By comparing the drilling outcomes and relative materials, our results show that the application of this integrated method is very effective.

Agricultural and urban land use change analysis in Changping County, Beijing, using remote sensing and GIS

Urban growth is regarded as a necessary transitional stage for a sustainable economy, but uncontrolled or arbitrary urban growth rapidly consumes rural resources and causes environmental pollution, ecological deterioration. In this paper, we developed a remote sensing and GIS-based integrated approach to monitor and analyze agricultural and urban spatial land use and ecological landscape change characteristics. In the proposed approach, multi-temporal satellite images from 1995 to 2010 were selected and classified to obtain land cover and use spatial changes. And GIS was used to analyze variation tendency for land use and ecological landscape indices. Experiments were performed in the Changping County, north of Beijing to analyze rapid urbanization effects in the past two decades, especially during the Beijing 2008 Olympic Games. The results indicate that there has been a notable urban growth and a visible loss about 38.8% in cropland, meanwhile dominated landscape structures and patterns have greatly changed from agriculture to urban in the study area.

Projection pursuit learning network algorithm for plant classification

Plant community is a significant content in the ecosystem. Traditional investigation method for plant community is mainly based on field sampling, which is limited by the data acquisition from complex terrain areas. In contrast, high-resolution remote sensing technique provides a convenient way to quickly access data in a large area. higher dimensional information is needed to distinguish more fine features. To overcome the shortcomings derived from the high dimensional features, which is caused by related data increasing, we choose the algorithm of projection pursuit learning network (PPLN) along with field samples of typical plant communities to realize a fast classification on the vegetation in the east of Shenzhen. Then, in the experiment, the spectral and texture information extracted from Pléiades images, and the terrain interpolated from topographic map are selected and used to build high dimensional features, which is crucial to the vegetation classification using remote sensing images. The learning network for projection pursuit is applied to discriminating the typical communities in both plantation and natural secondary forest in the study area. Compared with Maximum-likelihood classification (MLC) and Support Vector Machine (SVM), PPLN can achieve more accurate results for plant community classification. As a conclusion, the plant community classification with PPLN meets the requirements of the investigation project, achieves the quick updating of some basic information related to forest resources, and looks forward to involve in some other ecological research as well.

The application of landslide 3D measurement based on high resolution satellite stereo pairs

The main purpose of the present study is to discuss the technique and method of fast and effective investigation of geological disasters using remote sensing, and quantitatively assessing the landslide based on two temporal DEMs, which were extracted by using high-resolution IKONOS stereo images after the landslide and contour map before the landslide. This paper takes Daguangbao landslide as research object, researched the scope and scale of the landslides, and calculated the volume of the landslides analyzing the 3D features of the landslides, using the methods of remote sensing image 3D techniques. The area of Daguangbao landslide was calculated to be 7.82 km2,the length of the landslide was 4.5 km, the maximum width of landslide body was 3.6 km, and the volume is 10.23×108 m3.

The application of geoeye-1 stereo pair images to regional gravimetric terrain corrections

The accuracy of the gravity corrections is limited by the ability to survey the near-station topography. By now, regional gravity terrain corrections still rely on traditional high field survey, which is inefficient and expensive. In this paper, we explored an alternative way by using high resolution satellite stereo-photogrammetry for gravimetric terrain corrections. In the proposed approach, geoeye-1 stereo pair images with rational function model were used to obtain high precision and density digital terrain models near gravimetric points. Meanwhile, a differential equation was put forwards to compute the gravity terrain effect. Compared with field gravity surveying data, results illustrated that our model is effective for middle zone terrain correction, and has a good prospect for inner zone terrain correction.