Lingjia Gu

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.

An effective method for the detection and removal of thin clouds from MODIS image

Cloud is one of common noises in MODIS remote sensing image. Because of cloud interference, much important information covered with cloud cant be obtained. In this paper, an effective method is proposed to detect and remove thin clouds with single MODIS image. The proposed method involves two processing-thin cloud detection and thin cloud removal. As for thin cloud detection, through analyzing the cloud spectral characters in MODIS thirty-six bands, we can draw the conclusion that the spectral reflections of ground and cloud are different in various MODIS band. Hence, the cloud and ground area can be separately identified based on MODIS multispectral analysis. Then, the region label algorithm is used to label thin clouds from many candidate objects. After cloud detection processing, thin cloud removal method is used to process each cloud region. Comparing with traditional methods, the proposed method can realize thin cloud detection and removal with single remote sensing image. Additionally, the cloud removal processing mainly aims to the cloud label region rather than the whole image, so it can improve the processing efficiency. Experiment results show the method can effectively remove thin cloud from MODIS image.

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.

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.

Evaluation of spatial upscaling methods based on remote sensing data with multiple spatial resolutions

In most applications of remote sensing data, special spatial information is required from a finer to a coarser spatial resolution with appropriate upscaling methods. The purpose of this paper is to compare and evaluate current spatial upscaling methods using MODIS remote sensing data with multiple spatial resolutions. In the research, Northeast China was selected as the study area. MODIS data with spatial resolutions of 250 m (2 bands) and 500 m (7 bands) were used as the test data. Through using the selected upscaling methods, the Band 1 and Band 2 data of MODIS were scaled up from 250 m to 500 m spatial resolution. On the basis of land cover characteristics of Northeast China, the MODIS data located in the study area was classified into the five land cover types, including water, grasslands, forests, farmlands and bare lands using maximum likelihood method. The land cover classification results were further compared with MODIS Land Cover Type product. Finally, Structural Similarity (SSIM) was selected to evaluate the effects of these upscaling methods. The research can provide more useful information for spatial scaling transformation in remote sensing data applications.

Hyperspectral image lossless compression algorithm based on error compensated prediction tree of multi-band prediction

A new lossless compression method based on prediction tree with error compensation for hyperspectral imagery is proposed in this paper. This method incorporates the techniques of prediction tree and adaptive band prediction. The proposed method is different from previous similar approaches in that its prediction to the current band is performed by multiple bands and the error created by the prediction tree is compensated by a linear adaptive predictor for decorrelating spectral statistical redundancy. After de-correlating intraband and interband redundancy, the SPIHT (Set Partitioning in Hierarchical Trees) wavelet coding is used to encode the residual image. The proposed method achieves high compression ratio on the NASA JPL AVIRIS data.