Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

Stripes are common in remote sensing imaging systems equipped with multichannel time delay integration charge-coupled devices (TDI CCDs) and have different scale characteristics depending on their causes. Large-scale stripes appearing between channels are difficult to process by most current methods. The framework of column-by-column nonuniformity correction (CCNUC) is introduced to eliminate large-scale stripes. However, the worst problem of CCNUC is the unavoidable cumulative error, which will cause an overall color cast. To eliminate large-scale stripes and suppress the cumulative error, we proposed a destriping method via unidirectional multiscale decomposition (DUMD). The striped image was decomposed by constructing a unidirectional pyramid and making difference maps layer by layer. The highest layer of the pyramid was processed by CCNUC to eliminate large-scale stripes, and multiple cumulative error suppression measures were performed to reduce overall color cast. The difference maps of the pyramid were processed by a designed filter to eliminate small-scale stripes. Experiments showed that DUMD had good destriping performance and was robust with respect to different terrains

GF-4 Images Super Resolution Reconstruction Based on POCS

The super resolution reconstruction of GF-4 is made by projection on convex sets (POCS). Papoulis-Gerchberg is used to construct reference frame which can reduce iteration and improve algorithm efficiency.Vandewalle is used to estimate motion parameter which is benefit to block process. Tested and analyzed by real GF-4 series images, it shows that sharpness of super resolution result is positive correlatie to frame amount, and signal to noise ratio (SNR) is negative correlate to frame amount. After processing by 5 frames, information entropy (IE) changes little; sharpness (average gradient) increases from 7.803 to 14.386; SNR reduces a little, from 3.411 to 3.336. The experiment shows that after super resolution reconstruction, sharpness and detail information of results can be greatly improved

(<i>E</i>)-1-(5-(Hydroxymethyl) furan-2-yl)-4,4-dimethylpent-1-en-3-one

This study presents a novel approach in the realm of catalytic organic synthesis by integrating biomass catalytic conversion with organic synthesis techniques. Utilizing N-acetylglucosamine as the primary feedstock, the first phase of the research involves its catalytic transformation into 5-hydroxymethylfurfural (HMF). The subsequent phase employs a condensation reaction between HMF and 3,3-Dimethyl-2-butanone to synthesize a new compound, (E)-1-(5-(hydroxymethyl) furan-2-yl)-4,4-dimethylpent-1-en-3-one. This two-step process not only demonstrates the feasibility of converting biomass into valuable chemical precursors but also exemplifies the synthesis of novel compounds through green chemistry principles. The successful execution of this methodology offers fresh insights and opens new avenues for advancements in catalytic organic synthesis, emphasizing sustainability and efficiency

Panchromatic and Multi-spectral Fusion Method Combined with Adaptive Gaussian Filter and SFIM Model

Panchromatic and multi-spectral fusion technology can increase feature discriminant ability of remote sensing images.However,the abilities of fusing spatial information and keeping spectral information are conflict,and are hard to be balanced by common algorithms.SFIM (smoothing filter-based intensity modulation) can keep spectral information effectively,but is difficult to fuse spatial information which will reduces the holistic effect.Pointing to this problem,this paper analyzes the principles and characters of SFIM model,and proposes a fusion method combined with adaptive Gaussian filter and SFIM model (AGSFIM).Computing optimal parameter of Gaussian filter based on entirety mean-value-adjusted average gradient of multi-spectral bands,and adjusting down-sampled panchromatic image to same sharpness level which can confirm the balances of spatial information fusing ability and spectral information keeping ability.Beijing-2 and ZY-3 02 data are applied to test and six different fusion methods are used to compare.The experiments show that AGSFIM can effectively overcome SFIM's shortage and increase fusion images' spatial information

An Improved Jitter Detection Method Based on Parallax Observation of Multispectral Sensors for Gaofen-1 02/03/04 Satellites

Gaofen-1 02/03/04 satellites, the first civilian high resolution optical operational constellation in China, have Earth observation capabilities with panchromatic/multispectral imaging at 2/8 m resolution. Satellite jitter, the fluctuation of satellite points, has a negative influence on the geometric quality of high-resolution optical satellite imagery. This paper presents an improved jitter detection method based on parallax observation of multispectral sensors for Gaofen-1 02/03/04 satellites, which can eliminate the effect of the relative internal error induced by lens distortion, and accurately estimate the parameters of satellite jitter. The relative internal error is estimated by polynomial modelling and removed from the original parallax image generated by pixel-to-pixel image matching between two bands of images. The accurate relative time-varying error and absolute distortion caused by satellite jitter could be estimated by using the sine function. Three datasets of multispectral images captured by Gaofen-1 02/03/04 satellites were used to conduct the experiments. The results show that the relative system errors in both the across- and along-track directions can be modelled with a quadratic polynomial, and satellite jitter with a frequency of 1.1&ndash;1.2 Hz in the across-track direction was detected for the first time. The amplitude of the jitter differed in the three datasets. The largest amplitude, from satellite 04, is 1.3 pixels. The smallest amplitude, from satellite 02, is 0.077 pixels. The reliability and accuracy of the detection results were verified by using two groups of band combinations and ortho-images with a 1 m resolution. The comparison results show that the detection accuracy is improved by approximately 30% using the proposed method

Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening

This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS

Hyperspectral and Multispectral Remote Sensing Image Fusion Based on Endmember Spatial Information

Hyperspectral (HS) images usually have high spectral resolution and low spatial resolution (LSR). However, multispectral (MS) images have high spatial resolution (HSR) and low spectral resolution. HS&ndash;MS image fusion technology can combine both advantages, which is beneficial for accurate feature classification. Nevertheless, heterogeneous sensors always have temporal differences between LSR-HS and HSR-MS images in the real cases, which means that the classical fusion methods cannot get effective results. For this problem, we present a fusion method via spectral unmixing and image mask. Considering the difference between the two images, we firstly extracted the endmembers and their corresponding positions from the invariant regions of LSR-HS images. Then we can get the endmembers of HSR-MS images based on the theory that HSR-MS images and LSR-HS images are the spectral and spatial degradation from HSR-HS images, respectively. The fusion image is obtained by two result matrices. Series experimental results on simulated and real datasets substantiated the effectiveness of our method both quantitatively and visually

Image Fusion for High-Resolution Optical Satellites Based on Panchromatic Spectral Decomposition

Ratio transformation methods are widely used for image fusion of high-resolution optical satellites. The premise for the use the ratio transformation is that there is a zero-bias linear relationship between the panchromatic band and the corresponding multi-spectral bands. However, there are bias terms and residual terms with large values in reality, depending on the sensors, the response spectral ranges, and the land-cover types. To address this problem, this paper proposes a panchromatic and multi-spectral image fusion method based on the panchromatic spectral decomposition (PSD). The low-resolution panchromatic and multi-spectral images are used to solve the proportionality coefficients, the bias coefficients, and the residual matrixes. These coefficients are substituted into the high-resolution panchromatic band and decompose it into the high-resolution multi-spectral bands. The experiments show that this method can make the fused image acquire high color fidelity and sharpness, it is robust to different sensors and features, and it can be applied to the panchromatic and multi-spectral fusion of high-resolution optical satellites