Guojin He

A practical DOS model-based atmospheric correction algorithm

Atmospheric correction is of great importance in quantitative remote sensing studies. However, many of the atmospheric correction algorithms proposed in the literature are not easily applicable in real cases. In order to develop a practical atmospheric correction algorithm, Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is employed to obtain aerosol optical depth and the total atmospheric water vapour content, which are used to compute the transmittances in a dark object subtraction (DOS) model. An improved DOS atmospheric correction method combining MODIS imagery with the conventional DOS technique is proposed. A Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image acquired on 21 October 2001 in Wuyi mountain, south-eastern China, and a CBERS 02 CCD image acquired on 24 August 2005 in Dunhuang, north-western China, were atmospherically corrected with this new approach. Various tests are performed, from spectral signature analysis, to vegetation index spatial profile and image information content comparisons, and by direct comparison with ground-measured reflectances, to evaluate the performance of the improved DOS model. The evaluation shows it can generally achieve a good atmospheric correction result.

A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery

Due to the limited accuracy of exterior orientation parameters, ground control points (GCPs) are commonly required to correct the geometric biases of remotely-sensed (RS) images. This paper focuses on an automatic matching technique for the specific task of georeferencing RS images and presents a technical frame to match large RS images efficiently using the prior geometric information of the images. In addition, a novel matching approach using online aerial images, e.g., Google satellite images, Bing aerial maps, etc., is introduced based on the technical frame. Experimental results show that the proposed method can collect a sufficient number of well-distributed and reliable GCPs in tens of seconds for different kinds of large-sized RS images, whose spatial resolutions vary from 30 m to 2 m. It provides a convenient and efficient way to automatically georeference RS images, as there is no need to manually prepare reference images according to the location and spatial resolution of sensed images.

Generation of Landsat surface temperature product for China, 2000–2010

Land surface temperature (LST) is a key parameter in the physics of land surface processes on regional and global scales. Although there are MODIS and Landsat land surface reflectance products, there is no LST product for Landsat data due in part to many challenges in the development of an operational Landsat LST product generating system because Landsat possesses only one thermal infrared channel. The aim of this article is to describe the Landsat LST product generation project launched by the Centre for Earth Observation and Digital Earth (CEODE), Chinese Academy of Sciences. The generalized single-channel (SC) algorithm proposed by Jiménez-Muñoz et al. is used for LST retrieval. It is fully operational, requires minimal input data requirements, and has acceptable precision. Total atmospheric water vapour content is the key input parameter required by the SC algorithm. In this project, the MODIS water vapour product is employed to derive total atmospheric water vapour content. In this way, an operational Landsat LST product generation program was constructed by integration of MODIS and Landsat satellite imagery.

Assessing Light Pollution in China Based on Nighttime Light Imagery

Rapid urbanization and economic development inevitably lead to light pollution, which has become a universal environmental issue. In order to reveal the spatiotemporal patterns and evolvement rules of light pollution in China, images from 1992 to 2012 were selected from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and systematically corrected to ensure consistency. Furthermore, we employed a linear regression trend method and nighttime light index method to demonstrate China’s light pollution characteristics across national, regional, and provincial scales, respectively. We found that: (1) China’s light pollution expanded significantly in provincial capital cities over the past 21 years and hot-spots of light pollution were located in the eastern coastal region. The Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions have formed light pollution stretch areas; (2) China’s light pollution was mainly focused in areas of north China (NC) and east China (EC), which, together, accounted for over 50% of the light pollution for the whole country. The fastest growth of light pollution was observed in northwest China (NWC), followed by southwest China (SWC). The growth rates of east China (EC), central China (CC), and northeast China (NEC) were stable, while those of north China (NC) and south China (SC) declined; (3) Light pollution at the provincial scale was mainly located in the Shandong, Guangdong, and Hebei provinces, whereas the fastest growth of light pollution was in Tibet and Hainan. However, light pollution levels in the developed provinces (Hong Kong, Macao, Shanghai, and Tianjin) were higher than those of the undeveloped provinces. Similarly, the light pollution heterogeneities of Taiwan, Beijing, and Shanghai were higher than those of undeveloped western provinces.

RPC Estimation via l(1)-Norm-Regularized Least Squares (L1LS)

A rational function model (RFM), which consists of 80 rational polynomial coefficients (RPCs), has been widely used to take the place of rigorous sensor models in photogrammetry and remote sensing. However, it is difficult to solve the RPCs because of the requirement for numerous observation data [ground control points (GCPs)] in a terrain-dependent case and the strong correlation between the coefficients (ill-poseness). Regularization methods are usually applied to cope with the correlations between the coefficients, but only ℓ2-norm regularization is used by the existing approaches (e.g., ridge estimation and Levenberg-Marquardt method). The ℓ2-norm regularization can make an ill-posed problem well-posed but does not reduce the requirement for observation data. This paper presents a novel approach to estimate RPCs using ℓ1-norm-regularized least squares (L1LS) , which provides stable results not only in a terrain-dependent case but also in a terrain-independent case. On one hand, by means of L1LS, the terrain-dependent RFM becomes practical as reliable RPCs can be obtained by using much less than 40 or 39 (if the first denominators are equal to 1) GCPs, without knowing the orientation parameters of the sensor. On the other hand, the proposed method can be applied to directly refine the terrain-independent RPCs with additional GCPs: when a single or several GCPs are used, direct refinement performs similarly to bias compensation in image space; when more GCPs are available, the direct refinement can achieve comparable accuracy of the rigorous sensor model (better than conventional bias compensation in image space) .

Automatic Line Segment Registration Using Gaussian Mixture Model and Expectation-Maximization Algorithm

Line segment registration (LSR) for image pairs is a challenging task but plays an important role in remote sensing and photogrammetry. This paper proposes a line segment registration method using Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) algorithm. Comparing to the conventional registration methods which consider the local appearance of points or line segments, the proposed method of LSR uses only the spatial relations between the line segments detected from an image pair, and it does not require the corresponding line segments sharing the same start points and end points. Although the proposed method is not confined to the transformation model between the image pair, the affine model, which is a simple and fast registration model and widely used in remote sensing, is taken to verify the proposed method. Various images including aerial images, satellite images and GIS data are used to test the algorithm, and test results show that the method is robust to different conditions, including rotation, noise and illumination. The results of the proposed method are compared with those of other line segment matching methods, and it is shown that the proposed method is superior in matching precision and performs better in less-texture or no-texture case.

Towards an operational method for land surface temperature retrieval from Landsat 8 data

ABSTRACT Land surface temperature (LST) is a key parameter in the physics of land surfaces through the processes of energy and water exchange with the atmosphere. For Landsat data with only one thermal infrared channel (Landsat 4 to Landsat 7), LST cannot actually be retrieved, and external data sources, such as meteorological observations or Moderate Resolution Imaging Spectroradiometer (MODIS) data, are needed to obtain the water vapour content parameter (an important input parameter for the LST retrieval algorithm); this results in limitations on deriving LST. However, the band designations of the Landsat 8 sensors enable the derivation of LST from the Landsat 8 data. This article demonstrates an LST retrieval methodology that makes use of only Landsat 8 image data. In this methodology, the split-window covariance-variance ratio (SWCVR) technique is introduced to derive water vapour content from Landsat 8. A comparison between the retrieved LST and the in situ LST measurements shows good accuracy, with a root mean squared error (RMSE) of 0.83 K. The fact that the proposed LST estimation method utilizing solely Landsat 8 image data does not rely on any external data is a significant advantage for the development of an operational Landsat 8 LST product generating system.

A method of spatial mapping and reclassification for high-spatial-resolution remote sensing image classification.

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

Ongoing Conflict Makes Yemen Dark: From the Perspective of Nighttime Light

The Yemen conflict has caused a severe humanitarian crisis. This study aims to evaluate the Yemen crisis by making use of time series nighttime light images from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS). We develop a process flow to correct NPP-VIIRS nighttime light from April 2012 to March 2017 by employing the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light image. The time series analyses at national scales show that there is a sharp decline in the study period from February 2015 to June 2015 and that the total nighttime light (TNL) of Yemen decreased by 71.60% in response to the decline period. The nighttime light in all provinces also showed the same decline period, which indicates that the Saudi-led airstrikes caused widespread and severe humanitarian crisis in Yemen. Spatial pattern analysis shows that the areas of declining nighttime light are mainly concentrated in Sana’a, Dhamar, Ibb, Ta’izz, ’Adan, Shabwah and Hadramawt. According to the validation with high-resolution images, the decline in nighttime light in Western cities is caused by the damage of urban infrastructure, including airports and construction; moreover, the reason for the decline in nighttime light in eastern cities is the decrease in oil exploration. Using nighttime light remote sensing imagery, our findings suggest that war made Yemen dark and provide support for international humanitarian assistance organizations.

Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Much work has been reported in the literature on LAI estimation in boreal forests using remotely sensed imagery. However, few if any explicit LAI retrieval studies on bamboo forests in Asian subtropical monsoon-climate regions based on remote sensing technology have been performed. Our goal is to carry out a comparative study on the LAI estimation methods of bamboo forest in Fujian province, China, based on IRS P6 LISS 3 imagery. Both the traditional empirical–statistical approach and the newly proposed normalized distance (ND) method were employed in this study, and a total of 18 modelling parameters were regressed against ground-based LAI measurements. The results show that simple ratio (SR) is the best predictor for LAI estimation in this study area, with the highest R 2 (coefficient of determination) value of 0.68; modified simple ratio (MSR) and normalized difference vegetation index (NDVI) ranked second and third, respectively. The good performance of these three vegetation indices (VIs) can be explained by the ratioing principle. The overall good modelling performance of the ND method in our study area also indicates it is a promising method.