Chengfan Li

Analysis of artificial corner reflector’s radar cross section: a physical optics perspective

Among many of the differential interferometric synthetic-aperture radar technologies, artificial corner reflectors (ACR) are widely used in monitoring earthquake deformation and urban subsidence due to their relative stability on synthetic-aperture radar (SAR) acquisition. Apparently, the detection and extraction of ACRs on remotely sensed images would be of utmost importance. Many different geometric types of ACRs are designed to achieve maximum detection. Among them, dihedral, rectangle trihedral, and pyramidal ACRs are the most commonly employed. The cost and difficulty of deploying ACRs in the field, however, render comparison among the three types rather impractical, if not impossible. The current study attempts to tackle the issue from a physical optics perspective. Adopting radar cross section (RCS) as the measure of ACRs’ detectability, we examined the relationships between the ACRs’ RCS under vertical polarity with various parameters including the radar incident angles, width and heights of the ACRs and the azimuthal angles. The analyses indicate that under vertical polarity, among the three types of ACRs, the rectangle trihedral ACR is the most tolerant to its deploying surroundings. To verify the physical optics analysis results, we collected ENVISAT data from a variety of deployed ACRs in the Yan-Huai Basin, China, and derived their reflectance characteristics. The field data agree with the theoretical analyses. From this practice, it seems that the physical optics method might prove to be a rather economical and effective approach to design and select appropriate ACRs in field deployment.

Extraction of Urban Built-Up Land in Remote Sensing Images Based on Multi-sensor Data Fusion Algorithms

With the development of the high-resolution remote sensing technology, dynamic monitoring of urban built-up land use high-resolution remote sensing technology already become an important content of urban remote sensing. In this paper we select SPOT5 in 2003 and QuickBird in 2007 as data sources which covers research area, the urban built-up land information has been extracted through SAVI, PRWI and textural features, and analysis the extraction results. The results of this research show that the extraction method based on SAVI, PRWI, textural features could automatic (semi-automatic) extract urban built-up land effectively, and has the characteristics of simplicity, convenience and efficiency.

Remote Sensing Monitoring of Volcanic Ash Clouds Based on PCA Method

Volcanic ash clouds threaten the aviation safety and cause global environmental effects. It is possible to effectively monitor the volcanic ash cloud with the aid of thermal infrared remote sensing technology. Principal component analysis (PCA) is able to remove the inter-band correlation and eliminate the data redundancy of remote sensing data. Taking the Eyjafjallajokull volcanic ash clouds formed on 15 and 19 April 2010 as an example, in this paper, the PCA method is used to monitor the volcanic ash cloud based on MODIS bands selection; the USGS standard spectral database and the volcanic absorbing aerosol index (AAI) are applied as contrasts to the monitoring result. The results indicate that: the PCA method is much simpler; its spectral matching rates reach 74.65 and 76.35%, respectively; and the PCA method has higher consistency with volcanic AAI distribution.

Variational Bayesian independent component analysis for spectral unmixing in remote sensing image

Spectral unmixing is a key technology of optical remote sensing image analysis; it not only influences the accuracy of the extraction of land cover information and automatic classification of topographical objects, but also greatly hinders the development of quantitative remote sensing. Independent component analysis (ICA) is a statistical method which is recently developed to extract the independent linear components, and which can realize the extraction of endmembers as well as fractional abundances with little a priori knowledge. However, ICA still cannot process the correlations among the various components. To overcome this problem, variational Bayesian independent component analysis (VBICA) has been proposed to process optical remote sensing images. In the Bayesian framework, the separation of independent components of remote sensing image has finally been achieved with conditional independence standards of Bayesian network and approximate variational algorithm. In the simulative image and real AVIRIS hyperspectral remote sensing image, the VBICA algorithm demonstrates its better performance. The experiment’s results indicate that the proposed VBICA algorithm is feasible, which has obvious advantages and a good application prospect. The reason is that it can effectively overcome the correlations between the various components in remote sensing images and break through the limitations of traditional remote sensing images analysis. Last but not least, the VBICA algorithm is applied in the classification of the TM multispectral remote sensing images. Compared to basic maximum likelihood classification, principal component analysis and FastICA algorithms, VBICA improves the classification accuracy of remote sensing images, and contributes to the further extension of the application of ICA in remote sensing image analysis.

Extraction of urban vegetation from high resolution remote sensing image

The extraction of urban vegetation information is a focal study point of the city remote sensing. To address the limitations of urban regional scale and the features of extraction of urban vegetation from high resolution satellite image based on object-oriented approach, this paper presented a new approach to use segmentation of high-resolution remote sensing image and the fuzzy classification technique based on multi-thresholds method, and then forests, thin grassland, thick grassland were extracted accurately. The new object-based method performances were assessed using Kappa coefficients and overall accuracy. High accuracy (93.72?) and overall Kappa coefficient (0.8236) were achieved by this new method using Quickbird image; the experimental results demonstrate the new approach is simple for computation in urban regional scale.

Volcanic ash cloud detection from MODIS image based on CPIWS method

Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.

A New Detection Method of Volcanic Ash Cloud Based on MODIS Image

The volcanic ash can affect the global climate changes and aviation safety, and has become a hot topic for public security research. The satellite remote sensing sensor can quickly and accurately obtain the volcanic ash cloud information. However, the satellite image has pretty strong inter-band correlation and data redundancy. Principal component analysis (PCA) can overcome the inter-band correlation and data redundancy of satellite images and compress a large number of complex information effectively into a few principal components. Taking the Eyjafjallajokull volcanic ash cloud formed on 19 April 2010 for example, in this paper, the PCA method is used to detect the volcanic ash cloud based on moderate resolution imaging spectroradiometer (MODIS) image. The results show that: the PCA method can obtain the volcanic ash cloud from MODIS image; it is much simpler and the detected volcanic ash cloud has a good consistency with the previous research on the basis of spatial distribution and SO2 concentration.

Volcanic ash cloud detection from remote sensing images using principal component analysis

Abstract Thermal infrared remote sensing can quickly and accurately detect the volcanic ash cloud. However, remote sensing data have pretty strong inter-band correlation and data redundancy, both of which have decreased to a certain degree the detecting accuracy of volcanic ash cloud. Principal component analysis (PCA) can compress a large number of complex information into a few principal components and overcome the correlation and redundancy. Taking the Eyjafjallajokull volcanic ash cloud formed on April 19, 2010 for example, in this paper, the PCA is used to detect the volcanic ash cloud based on moderate resolution imaging spectroradiometer (MODIS) remote sensing image. The results show that: the PCA can successfully acquire the volcanic ash cloud from MODIS image; the detected volcanic ash cloud has a good consistency with the spatial distribution, SO 2 concentration and volcanic absorbing aerosol index (AAI).

Low-Light Image Enhancement Based on Guided Image Filtering in Gradient Domain

We propose a novel approach for low-light image enhancement. Based on illumination-reflection model, the guided image filter is employed to extract the illumination component of the underlying image. Afterwards, we obtain the reflection component and enhance it by nonlinear functions, sigmoid and gamma, respectively. We use the first-order edge-aware constraint in the gradient domain to achieve good edge preserving features of enhanced images and to eliminate halo artefact effectively. Moreover, the resulting images have high contrast and ample details due to the enhanced illumination and reflection component. We evaluate our method by operating on a large amount of low-light images, with comparison with other popular methods. The experimental results show that our approach outperforms the others in terms of visual perception and objective evaluation.

A New Fuzzy Clustering Method With Neighborhood Distance Constraint for Volcanic Ash Cloud

Remote sensing classification for volcanic ash cloud is a difficult task in the remote sensing application, and how to accurately obtain the volcanic ash cloud information from remote sensing image has become a key step in remote sensing classification of volcanic ash cloud. Aiming at the characteristics of the remote sensing images, via introducing the neighborhood pixels based on the classical fuzzy C-means clustering algorithm, this paper proposed a new fuzzy clustering remote sensing classification method with neighborhood distance constraint for volcanic ash cloud. This paper is tested from simulation texture image and moderate resolution imaging spectroradiometer remote sensing image, and finally explored the Sangeang Api volcanic ash cloud case on May 30, 2014. Our experiments show that the proposed method can effectively classify the volcanic ash cloud from remote sensing images, and the overall classification accuracy and Kappa coefficient reach 88.4% and 0.8064. To some extent, it overcomes the deficiency of the approaches in traditional volcanic ash cloud remote sensing classification.