Ruizhi Ren

Automatic Thick Cloud Removal for MODIS Remote Sensing Imagery

In this paper, an automatic method is proposed to realize thick cloud removal for Moderate Resolution Imaging Spectrum-radiometer (MODIS) remote sensing imagery. The proposed method can make full use of MODIS advantages of high temporal resolution and spatial resolution. The overlapping region can be detected by utilizing geographical information of the thick cloud data and no-cloud data, then SIFT detection and feature point matching are applied in the overlapping region, furthermore, the exact matching point pairs can be extracted with proper strategy. Based on these exact matching point pairs and the quadratic polynomial model, the rectified image can be obtained. Meanwhile, thick cloud regions are detected by the algorithm of multispectral image analysis, and then the images of thick cloud regions are replaced with the corresponding regions of the rectified image. Finally, radiance differences are eliminated for image visual effect. Experiment results demonstrate that the proposed method can effectively remove thick cloud from MODIS image, which can satisfy the demand of post-processing for remote sensing imagery.

A novel method of dynamic target detection

The shooting range test is an important field in modern weapon development. The modern weaponry is developing towards long distance and automation directions, therefore the shooting range test is put forward new higher requirements. A novel method of target detection based on the digital image processing technology is proposed in the paper. Experiments indicate the strategy is fit to the request of the dynamic target detection and tracking in the shooting range.

Land Salinization Classification Method using Landsat TM in Western Jilin Province of China

In the study of land salinization classification, researchers are most concerned about the distribution, area and degree of salinization. Traditional classification methods of land salinization only manually extract the sample data from the study area, which cannot obtain the classification results for large area. With the development of remote sensing technology, remote sensing data is often used to extract and analyze the information of saline-alkali soil. At present, most classification methods of land salinization utilize the spectral information of remote sensing data based on supervised classification or unsupervised classification, which still have some errors in the classification results. Combining the sample data with Landsat TM images, the Western Jilin Province of China was selected as the study area in this paper. Through analyzing the relationship between the spectral characteristics and the content of soil salinity of the sample data extracted from different types of saline-alkali land, a land salinization classification method using the decision tree was proposed. The experimental results demonstrated that the proposed method can supply more accurate classification information of land salinization, and further effectively monitor soil salinization changes for the study area.

An effective method of image registration for super-resolution

In Super-Resolution, the combination technique of frequency domain and the improved kerens method has been applied in the sub-pixel image registration. The method proved to be accurate in movement estimation within given precision, but the registration accuracy was affected by the relative parameters. Based on the traditional method, an effective method of image registration for Super-Resolution in the paper was proposed in the paper. The proposed registration method has good performance by introducing the registration evaluation parameter. The experimental results demonstrate that the proposed method is effective for different test images, which takes into account the precision of estimation results and the computation efficiency as well.

Retrieval of snow depth in northeast China using FY-3B/MWRI passive microwave remote sensing data

Comparing with optical remote sensing techniques, passive remote sensing data have been proved to be effective for observing snowpack parameters such as snow depth and snow water equivalent, which can penetrate snowpack without clouds interferences. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3B (FY-3B) satellite is gradually used in the global environment research through November, 2011. In this paper, we proposed a snow depth retrieval algorithm to estimate snow depth in Northeast China using MWRI passive microwave remote sensing data. A decision tree method of snow identification was firstly designed to distinguish different snow cover conditions in order to eliminate other interference signals. After using the proposed decision tree method, the processing results were further used to retrieve the snow depth in Northeast China. Finally, the practical snow depth data and the MODIS data were collected for the accuracy assessment of the proposed snow depth retrieval method. The experimental results demonstrated that the RMSE of snow depth used the proposed method was approximately 3 cm in Northeast China.

Stripe noise removal method for MODIS remote sensing imagery

Stripe noise seriously influences the quality of remote sensing imagery, an effective method for removing stripe noise in MODIS (Moderate Resolution Imaging Spectroradiometer) imagery is proposed in this paper. The proposed method mainly considers the scanning characteristic of multi-detectors in MODIS. Utilizing the high correlation between detector subimages to predict the new detector subimages, then use these new detector subimages to compose the destriped image. Experimental results prove the proposed method is superior to present destriping methods, which can remove stripe noise well and preserve most information of original image. The proposed method is also applicable in stripe noise removal of other multi-detectors remote sensing imagery.

Snow Cover Identification of Saline-alkali Land in the Western Jilin Province of China based on MWRI Data

Snow parameters are important physical quantities of climatology and hydrology research, improving the accuracy of snow parameters is important for climatology, hydrology and disaster prevention and reduction. The western Jilin Province of China has obvious salinization problem. Meanwhile, it belongs to a typical snow-covered area. In this paper, the western Jilin Province is selected as the study area and the main research focuses on analyzing the snow cover conditions. The FY3B-MWRI passive microwave remote sensing data from year 2011 to 2016 are selected as experimental data. Compared with optical remote sensing data, using MWRI data can better obtain snow information, and it is also the preliminary work to retrieve snow depth and snow water equivalent. Furthermore, a new decision tree algorithm for snow cover identification was built to distinguish different snow cover conditions. Compared with the existing three algorithms reported in other literatures, the proposed algorithm improves the identification accuracy of snow cover up to 95.06%. While the accuracy for Singh’s algorithm, Pan’s algorithm and Li’s algorithm were about 80.19%, 78.79% and 90.13%, respectively. This study provides important information to the research of snow cover in saline-alkali land.

Saline-alkali land classification using MODIS data in Western Jilin Province, China

Remote sensing technology can extract useful information from observation areas, meanwhile provide effective data for land monitoring, which is widely used in dynamic monitoring and resources research of saline alkali land. Through using MODIS spectral remote sensing data, a case study of Western Jilin Province of China mainly covered by typical saline alkali land was carried out in this paper. After using the proposed optimal band combination method, the main distribution positions of the observed saline alkali land were roughly determined based on the colors and shapes of MODIS images derived from deferent seasons. After analyzing the time series of NDVI observations, the decision tree classification of land cover was designed to determine the land cover types and the degree of salinity-alkalinity. Through obtaining and analyzing of the spectral characteristics of each saline alkali land type, the relationship between the spectral characteristics and saline alkali land type was deduced. The research results demonstrated that the saline alkali lands located in Western Jilin Province, China were effectively classified based on the spectral characteristics of MODIS data, which provided the moderate spatial resolution classification results for a wide range of saline alkali land monitoring.

Clouds and Clouds Shadows Detection and Matching in MODIS Multispectral Satellite Images

Due to weather influence, it is difficult to obtain cloud-free images in MODIS multispectral remote sensing data. Most remote sensing images are more or less influenced by clouds and cloud shadows in data acquisition processing, which cause serious problems for data application. As a result, many researchers have presented effective methods to detect and remove these clouds and their shadows from remote sensing images. However, there is still important clouds three-dimensional information included in cloud shadows based on the principle of shadow imaging, for example, cloud height. Therefore, through analyzing and extracting the features of clouds and cloud shadows, the information of cloud height can be further detected. In this paper, clouds and cloud shadows detection and matching methods are discussed for MODIS multispectral satellite data. The research results can be further applied to detect cloud height, which support wider application fields for remote sensing data application.

A lossless compression method based on mix coding and IWT for MODIS image

In order to effectively store and transmit MODIS multispectral data, a lossless compression method based on mix coding and integer wavelet transform (IWT) is proposed in this paper. Firstly, the algorithm computes the correlation coefficients between spectrums in MODIS data. Using proper coefficient threshold, the original bands will be divided two groups: one group use spectral prediction method and then compress residual error, while the other group data is directly compressed by some standard compressor. For the spectral prediction group, we can find the current band that has greatest correlation with the previous band by the judgments of correlation coefficient, thus the optimal spectral prediction sequence is obtained by band reordering. The prediction band data can be computed with the previous band data and optimal linear predictor, so the spectral redundancy can be eliminated by using spectral prediction. In order to reduce residual differences in further, the block optimal linear predictor is designed in this paper. Next, except for the first band of the spectral prediction sequence, the residual errors of other bands are encoded by IWT and SPIHT. The direct compression bands and the first band of spectral prediction sequence are compressed by JPEG2000. Finally, the coefficients of block optimal linear predictor and other side information are encoded by adaptive arithmetic coding. The experimental results show that the proposed method is efficient and practical for MODIS data.