BASIC THEORY AND APPLICATIONS OF WELDING ARC SPECTRAL INFORMATION

Welding arc spectral information is a rising welding information source. In some occasion, it can reflect many physical phenomena of welding process and solve many problems that cannot be done with arc electric information, acoustic information and other arc information. It is of important significance in developing automatic control technique of welding process and other similar process. Many years study work on welding arc spectral information of the anthor are discussed from three aspects of theory, method and application. Basic theory, view and testing methods of welding arc spectral information has been put forward. In application aspects, many applied examples, for example, monitoring of harmful gases in arc （such as hydrogen and nitrogen） with the method of welding arc spectral information; welding arc spectral imaging of the welding pool which is used in automatic seam tracking; controlling of welding droplet transfer with welding arc spectral information and so on, are introduced. Especially, the successful application in real time controlling of welding droplet transfer in pulsed GMAW is introduced too. These application examples show that the welding arc spectral information has great applied significance and development potentialities. These .content will play an important role in applying and spreading welding arc spectral informarion technology.

Color Restoration Method Based on Spectral Information Using Normalized Cut

This paper proposes a novel method for color restoration that can effectively apply accurate color based on spectral information to a segmented image using the normalized cut technique. Using the proposed method, we can obtain a digital still camera image and spectral information in different environments. Also, it is not necessary to estimate reflectance spectra using a spectral database such as other methods. The synthesized images are accurate and high resolution. The proposed method effectively works in making digital archive contents. Some experimental results are demonstrated in this paper.

BASIC THEORY AND METHOD OF WELDING ARC SPECTRAL INFORMATION

Arc spectral information is a rising information source which can solve manyproblems that can not be done with arc electric information and other arc information. It is ofimportant significance to develop automatic control technique of welding process. The basic theoryand methods on it play an important role in expounding and applying arc spectral information. Usingconcerned equation in plasma physics and spectrum theory, a system of equations including 12equations which serve as basic theory of arc spectral information is set up. Through analyzing ofthe 12 equations, a basic view that arc spectral information is the reflection of arc state andstate variation, and is the most abundant information resource reflecting welding arc process isdrawn. Furthermore, based on the basic theory, the basic methods of test and control of arc spectralinformation and points out some applications of it are discussesed.

Estimation model of winter wheat disease based on meteorological factors and spectral information

Wheat scab(WS,Fusarium head blight),one of the most severe diseases of winter wheat in Yangtze-Huaihe river region,whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and achieve the purpose of reducing yield loss.In the present study,remote sensing monitoring on WS was conducted in 4 counties in Yangtze-Huaihe river region.Sensitive factors of WS were selected to establish the remote sensing estimation model of winter wheat scab index(WSI)based on interactions between spectral information and meteorological factors.The results showed that:1)Correlations between the daily average temperature(DAT)and daily average relative humidity(DAH)at different time scales and WSI were significant.2)There were positive linear correlations between winter wheat biomass,leaf area index(LAI),leaf chlorophyll content(LCC)and WSI.3)NDVI(normalized difference vegetation index),RVI(ratio vegetation index)and DVI(difference vegetation index)which had a good correlation with LAI,biomass and LCC,respectively,and could be used to replace them in modeling.4)The estimated values of the model were consistent with the measured values(RMSE=5.3%,estimation accuracy=90.46%).Estimation results showed that the model could efficiently estimate WS in Yangtze-Huaihe river region.

Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network

Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.

Solution of Multiple-Point Statistics to Extracting Information from Remotely Sensed Imagery

Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information. Therefore, there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery. In the current article, a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification. The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese Yellow River delta on August 8, 1999. The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier （MLC）.

Toward Circumventing Collinearity Effect in Nonlinear Spectral Mixture Analysis by Using a Spectral Shape Measure

Nonlinear spectral mixture analysis (NSMA) is a widely used unmixing algorithm. It can fit the mixed spectra adequately, but collinearity effect among true and virtual endmembers will decrease the retrieval accuracies of endmember fractions. Use of linear spectral mixture analysis (LSMA) can effectively reduce the degree of collinearity in the NSMA. However, the inadequate modeling of mixed spectra in the LSMA will also yield retrieval errors, especially for the cases where the multiple scattering is not ignorable. In this study, a generalized spectral unmixing scheme based on a spectral shape measure, i.e. spectral information divergence (SID), was applied to overcome the limitations of the conventional NSMA and LSMA. Two simulation experiments were undertaken to test the performances of the SID, LSMA and NSMA in the mixture cases of treesoil, tree-concrete and tree-grass. Results demonstrated that the SID yielded higher accuracies than the LSMA for almost all the mixture cases in this study. On the other hand, performances of the SID method were comparable with the NSMA for the tree-soil and tree-grass mixture cases, but significantly better than the NSMA for the tree-concrete mixture case. All the results indicate that the SID method is fairly effective to circumvent collinearity effect within the NSMA, and compensate the inadequate modeling of mixed spectra within the LSMA.

Lithological Mapping Using Landsat 8 OLI in the Meso-Cenozoic Tarfaya Laayoune Basin (South of Morocco): Comparison between ANN and SID Classification

In the Saharian domain, the Tarfaya-Laayoune coastal basin developed in a stable passive margin, where asymmetrical sedimentation increase from East to West and reach a sediment stack of about 14 kilometers. However, the morphology of the studied area corresponds to a vast plateau (hamada) presenting occasional major reliefs. For this purpose, remote sensing approach has been applied to find the best approaches for truthful lithological mapping. The two supervised classification methods by machine learning (Artificial Neural Network and Spectral Information Divergence) have been evaluated for a most accurate classification to be used for our lithofacies mapping. The latest geological maps and RGB images were used for pseudo-color groups to identify important areas and collect the ROIs that will serve as facilities samples for the classifications. The results obtained showed a clear distinction between the various formation units, and very close results to the field reality in the ANN classification of the studied area. Thus, the ANN method is more accurate with an overall accuracy of 92.56% and a Kappa coefficient is 0.9143.

Multi-spectral remote sensing image enhancement method based on PCA and IHS transformations

This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery,based on principal component analysis(PCA) and intensity-hue-saturation(IHS) transformations.The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component,respectively.The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation,and undertaking the inverse IHS transformation.The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image.On the basis of the visual and statistical analysis results of the experimental study,we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion.It has potential advantages in image mapping optimization,object recognition,and weak information sharpening.

Citrus black spot detection using hyperspectral imaging

This paper describes the development of a hyperspectral imaging approach for identifying fruits infected with citrus black spot(CBS).Hyperspectral images were taken of healthy fruit and those with CBS symptoms or other potentially confounding peel conditions such as greasy spot,wind scar,or melanose.Spectral angle mapper(SAM)and spectral information divergence(SID)hyperspectral analysis approaches were used to classify fruit samples into two classes:CBS or non-CBS.The classification accuracy for CBS with SAM approach was 97.90%,and 97.14% with SID.The combination of hyperspectral images and two classification approaches(SID and SAM)have proven to be effective in recognizing CBS in the presence of other potentially confounding fruit peel conditions.The study result can be a reference for the non-destructive detection of fruits infected with citrus black spot.