Yalan Liu

A Novel Vehicle Detection Method With High Resolution Highway Aerial Image

A robust and efficient vehicle detection method from high resolution aerial image is still challenging. In this paper, a novel and robust method for automatic vehicle detection using aerial images over highway was presented. In the method, a GIS road vector map was used to constrain the vehicle detection system to the highway networks. After the morphological structure element was identified, we utilized the grayscale opening transformation and grayscale top-hat transformation to identify hypothesis vehicles in the light or white background, and used the grayscale closing transformation and grayscale bot-hat transformation to identify the hypothesis vehicles in the black or dark background. Then, targets with large size or covering a large area were sieved from the hypothesis vehicles using an area threshold that is much larger than a typical vehicle. Targets, whose width is narrower than the diameter of structure element utilized in the grayscale morphological transformation, were smoothed out from the hypothesis vehicles using binary morphological opening transformation. Finally, the hypothesis vehicles detected in both cases were overlaid. It should be noted that in the detection system, a vehicle could be detected twice by the two approaches. The two identical hypothesis vehicles should be amalgamated into a single one for accuracy assessment subsequently. We tested our system on seventeen highway scenes of aerial images with a spatial resolution of 0.15 × 0.15 m. The experimental results showed that the correctness, completeness, and quality rates of the proposed vehicle detection method were about 98%, 93%, and 92%, respectively. Thus, our proposed approach is robust and efficient to detect vehicles of highway using high resolution aerial images.

Disaster monitoring and early-warning system for snow avalanche along Tianshan Highway

In this paper, a pilot study on the establishment of snow avalanche disaster monitoring and early-warning system based on the case study of area along the Tianshan Highway is presented. Prototype system design method is introduced into the whole process of early-warning system design and development based on the analysis of snow avalanche occurrence mechanism. Snow avalanche disaster databases and snow avalanche danger analysis model library are constructed, and the early-warning system which integrates the interfaces of database Access and model library access is developed by ArcGIS Engine 9.2 and can carry out the full workflow of early-warning information publication service. The Analytic Hierarchy Process (AHP) method is introduced in building snow avalanche danger analysis model which is incorporated into early-warning system.

Classification based on deep convolutional neural networks with hyperspectral image

Hyperspectral image (HSI) is usually composed of hundreds of bands which contain very rich spatial and spectral information. However, the high-dimensional data may lead to the curse of dimensionality phenomenon when it is used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we developed a deep learning classification framework based on the spectral and spatial information of hyperspectral image. Firstly, the deep learning features in different layers could be extracted automatically. Secondly, based on the learned deep learning features, we could obtain the classification of hyperspectral image with logistic regression (LR) classifier. Finally, we compared our approach with other methods including quadratic discriminant analysis with the multilevel logistic spatial prior (QDAMLL), logistic discriminant analysis with the multilevel logistic spatial prior (logDAMLL), linear discriminant analysis with the multilevel logistic spatial prior (LDAMLL), subspace multiclass logistic regression with the multilevel logistic spatial prior (MLRsub MLL), support vector machine on extended morphological profiles (SVM/EMP), support vector machine on expectation maximization and post-regularization (SVM-EM-PR). The experimental results showed that our method obtained the optimum accuracy, which was better than the other six approaches. And the OA was up to 99.39%. Therefore, the deep convolutional neural networks (DCNNs) is a robust method for land use classification with hyperspectral image. Index Terms — Classification; deep convolutional neural networks; hyperspectral image.

The application of ant colony algorithm in emergency rescue with GIS

Under the indoor building environment, when the fires and other accidents occur, how to effectively organize the masses evacuation and fire rescue, is closely related to the safety of peoples lives and property and has become a critical problem of public concern. This paper presents an improved ant colony algorithm (ACO) to solve the problem of how to optimize the evacuation route and rescue route when an accident occurs. According to the key factors affecting people emergency evacuation, such as indoor building environment, fire and its combustion products, problem of paths optimal selection, etc., we propose an emergency evacuation model, based on the model it can give an optimal evacuation route for the mass and an optimal rescue route for the firefighters. We also analyzes the search results, it shows that the search results is robust and reasonable.

Damaged road extracting with high-resolution aerial image of post-earthquake

With the rapid development of earth observation technology, remote sensing images have played more important roles, because the high resolution images can provide the original data for object recognition, disaster investigation, and so on. When a disastrous earthquake breaks out, a large number of roads could be damaged instantly. There are a lot of approaches about road extraction, such as region growing, gray threshold, and k-means clustering algorithm. We could not obtain the undamaged roads with these approaches, if the trees or their shadows along the roads are difficult to be distinguished from the damaged road. In the paper, a method is presented to extract the damaged road with high resolution aerial image of post-earthquake. Our job is to extract the damaged road and the undamaged with the aerial image. We utilized the mathematical morphology approach and the k-means clustering algorithm to extract the road. Our method was composed of four ingredients. Firstly, the mathematical morphology filter operators were employed to remove the interferences from the trees or their shadows. Secondly, the k-means algorithm was employed to derive the damaged segments. Thirdly, the mathematical morphology approach was used to extract the undamaged road; Finally, we could derive the damaged segments by overlaying the road networks of pre-earthquake. Our results showed that the earthquake, broken in Yaan, was disastrous for the road, Therefore, we could take more measures to keep it clear.

Establishment of rocky desertification index in Southwest of China

Rocky desertification is a type of land desertification. It comes from the fragile ecological and geological environment, where the human activity is very strong and the land productivity is degraded severely. As a natural disaster, rocky desertification is very destructive, and it is very difficult to be recovered. The karst region of China in southwest is the worlds concentrated karsts region. It is also one of the largest contiguous karsts regions. The karst region is also the most typical ecological fragile regions in China. We utilized the ETM+ images in 2000 to study the rocky desertification of the north regions in Guangxi province in the past ten years. Firstly, the rocky exponential model was established to extract rocky desertification information of the region. Then, the RGB image was composited to interpret and obtain the rocky desertification. Our experiment showed that rocky desertification of the karst region can be classified into no rocky desertification, moderate desertification, and severe rocky desertification.

Vehicle detection of parking lot with different resolution aerial images

Vehicle detection is a very important task for intelligent transportation system. In this paper, a method with mathematical morphology and template matching is presented to detect the crowded vehicles of parking lot with high resolution aerial image. Our experimental results with high resolution aerial image showed that the graded image, with the spatial resolution of 1×1ft, could greatly reduce the calculation time, but with the same accuracy as the original image with the spatial resolution of 0.5×0.5ft .

Vehicle detection from parking lot aerial images

Vehicle detection from high resolution aerial images has been studied for many years. However, a robust and efficient vehicle detection is still challenging. In this paper, a novel and robust method for automatic vehicle detection from aerial images was presented. In this method, a GIS road vector map is used to constrain a vehicle detection system to parking lot networks, edge detection and morphological preprocessing method are used to identify candidate vehicle pixels. Different types of vehicle templates are selected to adaptively detect the similar vehicles by their correlation coefficient with the same size of the window. Experiment was conducted using 0.15 meter resolution aerial images, the result demonstrated that the new method had an excellent detection performance.

A semi-analytic method to speed up the convergence of successive order of scattering model

While the successive order of scattering (SOS) method is used to solve the radiative transfer equation, however, for the large optical depth with a high single scattering albeldo, the slow convergence will spend a lot of computing time. To speed up the convergence of SOS method, an improved semi-analytic model is developed and the two notes fitting method is used to get the ratio of two successive scattering radiances after the thirteenth scattering. The new semi-analytic model is accurate and efficient and makes the SOS method applicable for optically thick scattering media. With the improved semi-analytic model, the efficiency of the SOS method can be greatly improved.

A study on land cover classification based on HJ-1 CCD image

Remote sensing technique has become one of the most effective means to acquire land cover information. The CCD cameras onboard the environment and disaster monitoring and forecasting satellite constellation (HJ-1 satellites) are advanced in spatial resolution, image coverage and revisit frequency. Thus, it can be very efficient to do land cover classification using their images. Taking Zhongshan County in Guangxi province as the study area, this paper studies on the best method of land cover classification based on HJ-1 CCD image and other assistant data. In order to test the general applicability of this land cover classification method, this paper also applies the method to another place.