Yunping Chen

Automatic power line extraction from high resolution remote sensing imagery based on an improved Radon transform

In this paper, we propose a new algorithm for power line identification and extraction from high resolution remote sensing images. Theoretically, it is difficult to detect power lines in satellite images due to some characteristics, such as sub-pixel, weak target, discrete and the complicated background. To our knowledge, the problem of extraction of the power lines from satellite images is faced for the first time. An improved Radon transform, Cluster Radon Transform (CRT), was developed to extract linear feature from satellite image. Compared with conventional Radon transform, CRT can efficiently avoid false alarm. After that, a set of rules of power lines was abstracted to distinguish power lines from other linear feature, such as roads. The experimental results show that CRT not only has strong anti-noise capability to random noise, but also has strong anti-noise capability to system noise caused by non-linear feature. Furthermore, CRT also has the strong capability to detect short segment in an image. Finally, synthetic images and true images were used to verify the new approach. The achievement has potential to be applicable not only to the power line extraction, but also to other weak linear target detection. An improved Radom transform, Cluster Radon Transform, for weak linear and short segment feature extraction is proposed.A distinguish algorithm for power line extraction are developed.A scheme for power line detection is constructed.

Estimating ground-level PM2.5 concentration using Landsat 8 in Chengdu, China

An empirical multilinear model was developed for estimating ground-level PM2.5 concentration at city scale (Chengdu, China) using Landsat 8 data. In this model, the improved DDV (dense dark vegetation) algorithm (V5.2) was used to retrieve aerosol optical thickness (AOT), Image-based Method (IBM) was used to compute the land surface temperature (LST), and TVDI was calculated to reflect the air humidity. The three parameters (AOT, LST, TVDI) and in-situ measured PM2.5 (particulate matter) data were then utilized to establish the empirical model by partial least square (PLS) regression. In the computation, the band 9, cirrus band, was used to reduce the influence of atmospheric vapor to LST retrieval. The results show that when considering moisture and temperature, the correlation between AOT and PM2.5 would be efficiently improved; furthermore, moisture shows more impact on the relationship than temperature. Station record hourly average PM2.5 also shows higher correlation coefficients than 24-hr average. As a result, PM2.5 concentration distribution across Chengdu was retrieved using this model developed in this paper. The method could be a beneficial complement to ground-based measurement and implicate that remote sensing data has enormous potential to monitor air quality at city scale.

Air Pollution Sources Identification Precisely Based on Remotely Sensed Aerosol and Glowworm Swarm Optimization

In this paper, we developed a novel method to identify air pollution sources based on remotely sensed aerosol data and Glowworm Swarm Optimization (GSO). In practice, it is usually to identify the air pollution sources to certain industries, such as transportation, power plants, biomass burning, and et.al. To our knowledge, the problem of locating and quantifying the pollution to the specified factories is faced for the first time. In this study, the aerosol retrieved from remotely sensed image and GIS were used to locate and quantify the pollution to each enterprise in the study area based on an improved Glowworm Swarm Optimization and meteorological condition. As a result, the gross and intensity of every enterprise in the study area were achieved. Therefore, the polluting contribution of each factory could be listed, and the most polluting factories could be found. Some experiments were carried out to validate the method, and the Key monitoring factories by local authority was ferreted out accurately.

Spatial scale conversion approach for moderate-resolution imaging spectroradiometer leaf area index product validation

Abstract This paper proposes a new spatial scale conversion method, which validates moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) product when geometry information from the MODIS 1B product and classification result is combined. The in situ LAI data, Landsat Thematic Mapper (TM), and MODIS 1B product were utilized in this research. An object-oriented method was used to classify TM imaging, where each class was computed using an empirical model to achieve LAI respectively. The 30-m TM LAI image was aggregated into the MODIS 1B product based on the geometry information of MODIS 1B. The simulated MODIS 1B image was then converted into a MODIS LAI product and compared with the simulated LAI map pixel by pixel. The results showed a lower root mean square error and higher normalization of the absolute error with the new method. In addition, the field LAI was not significantly correlated with MODIS LAI, but it did show a strong correlation with TM LAI. The new method achieved a higher correlate coefficient with the MODIS product than the conventional methods. Using this validation method based on classification and image simulation can improve the accuracy of product certification.

GIS-based city noise mapping research and development

In todays society, as town roads and construction infrastructure has been improved gradually, the noise pollution has effected the environment more and more seriously. The noising map which has unique way to show the distribution of noise in the real-time and can monitor the situation of the noise effectively, it can let user master the situation of noise pollution clearly, and the application of noising map has become a main research direction of denoising work in recent years. This paper mainly analyzed the domestic and foreign research of the noising map and the main noise prediction method, selected the RLS90 model to develop the software. In the development, we used the ArcGIS Engine setups and Visual Studio programming environment, using the object-oriented development method and the GIS component second development function to develop a relatively perfect function noise prediction and noising map generation system based on the geographic information system.

A new method of leaf area index measurement based on the digital images

With the development of technology of digital camera, the performance of image sensor is continuously improving, which makes it possible to measure leaf area index by digital photos in a simpler and cheaper way compared with the other ways. This article proposed a new way to measure LAI by using digital images, and the formulation of measuring LAI is deduced from both the principle of LAI2000 and theory of digital camera. Considering the system error, the modified formula is also deduced. In this article, there are about 35 measurement locations which include kinds of different density of canopy, where the LAI were measured by both proposed method and professional instrument LAI2000 to validate the accuracy. The experiment results show that the correlation coefficient was 0.9794 between the proposed method and LAI2000, which proved the accuracy and feasibility of measurement of LAI based on digital photos. The new method has a bright application prospect.

A New Air Pollution Source Identification Method Based on Remotely Sensed Aerosol and Improved Glowworm Swarm Optimization

Air pollution sources generally cannot be identified as the specific factories but certain industries. Focusing on this issue, a new method, based on an improved glowworm swarm optimization and remotely sensed imagery, was proposed to precisely orientate and quantify air pollution sources in this study. In addition, meteorological data and GIS information were also used to backtrack the pollution source. After that, in order to quantify the pollution of each factory in the study areas, three pollution indices, pollution gross (PG), pollution intensity, and area-normalized pollution (ANP), were proposed. As a result, the polluting contribution of each factory was listed, and the most polluting factories, which were bulletined as the key monitoring factories by the local authority, were accurately extracted. Among the pollution indices, ANP is the most robust, reliable, and recommended. Furthermore, the result also shows factory pollution background information achieved from the historical remote sensing data which can be used to improve the precision of identification. To our knowledge, this study provides the first attempt to address the problem of identifying a pollution source as originating from an individual factory based on remote sensing data. The proposed method provides a useful tool for air quality management, and the result would be meaningful to environmental and economic issue.

A new air pollution sources identification method based on remotely sensed aerosol and swarm intelligence

In this paper, a novel method was developed to orientate and quantify the air pollution sources based on remotely sensed aerosol data and Glowworm Swarm Optimization (GSO). In practice, based on source apportionment technique, the air pollution sources could just be identified to certain industries, such as transportation, power plants, biomass burning, and et.al. To our knowledge, the problem of orientating and quantifying the pollution to the individual factories is faced for the first time. In this study, the aerosol retrieved from remotely sensed image (MODIS) and GIS were used to locate and quantify the pollution to each enterprise in the study area based on an improved Glowworm Swarm Optimization and meteorological condition. As a result, the polluting contribution of each factory were be listed, and the most polluting factories were be found. Some experiments were carried out to validate the method, and the Key monitoring factories by authority was ferreted out accurately.

A new aerosol retrieval algorithm based on statistical segmentation using Landsat-8 OLI data

In this paper, a new aerosol retrieval algorithm based on a method, named statistical segmentation, was proposed. Firstly, the image of Landsat 8 OLI was divided into many segments by the statistical segmentation method based on band 6 and band 7. Then, according to the characteristics of the segmentation, two ways based on the segmented results were used to get the surface reflectance. And then combined with the apparent reflectance equation and a lookup table built by 6S model, aerosol retrieval could be performed. In principle, this algorithm is based on clean pixels (almost no aerosol) at band 1 to retrieve contaminated pixels in the same segment. The retrieved results show that, compared with DDV (Dense Dark Vegetation) algorithm, this algorithm is more suitable for bright surfaces, such as urban areas.

A new algorithm for aerosol retrieval using HJ-1 CCD and MODIS NDVI data over urban areas

Aerosol retrieval over urban areas is a difficult task because of the high reflectance of the underlying surface. In this paper, a new aerosol retrieval algorithm based on the spectral analysis of soil and vegetation from spectral library was proposed, the simulated correlation between the normalized difference vegetation index (NDVI) and the surface reflectance of red, blue bands was established. And also to solve scale problem, a conversion method based on maximizing mutual information (MI) was used. The algorithm was applied to Beijing city using the China HJ-1A/1B of the Environment and Disaster Monitoring Microsatellite Constellation Charge-Coupled Device (CCD) and MODIS NDVI data. The result, aerosol optical thickness (AOT) with a 100m×100m resolution, was compared to the ground measurement data from Aerosol Robotic Network (AERONET), which shows a high consistency with observation data, and the overall correlation coefficient of approximately 0.935 and a root-mean-square error (RMSE) of about 0.34. The algorithm is very useful and significant for environmental protection and air quality monitoring over urban areas.