The salient object detection of optical remote sensing images (ORSI-SOD) is an important research direction in ORSI processing, which has achieved promising results in the last few years. Many recent works heavily rely on feature learning of regions for the improvement of the detection accuracy, while neglecting the concrete role of edge and skeleton information in the calculation. In this work, we propose a two-stage edge and skeleton guidance network (ESGNet) for ORSI-SOD in a coarse-to-fine way, and further demonstrate that the fused features of edge and skeleton are essential for ORSI-SOD. In the first stage, we construct the spatial graph attention (SGA) module for saliency features to generate an initial saliency map, and apply the spatial self-optimization (SSO) to enhance edge and skeleton features. The multi-level interactive fusion (MIF) module is used for the adequate integration of edge and skeleton features into saliency features. In the second stage, with the aim to accomplish better prediction of salient object localization and shape, the feature enhancement integration operation is introduced to recover object details from the learned edge and skeleton features. Extensive experiments on three public ORSI-SOD datasets demonstrate that our ESGNet achieves competitive performance with the state-of-the-art methods and also confirms the importance of edge and skeleton information for ORSI-SOD. Meanwhile, generalizability experiments on natural image datasets show that our method is competent for many types of SOD tasks. The code and results of our method are available at https://github.com/aoao0206/ESGNet.

Laser-induced breakdown spectroscopy (LIBS) faces a notable obstacle in the presence of substantial spectral fluctuations, hindering its progress and prompting researchers to prioritize resolving this complex problem. Considering the circumstances above, a novel methodology referred to as spectral standardization based on plasma image-spectrum fusion (SS-PISF) was introduced. The approach utilizes the valuable information in plasma images and spectra to correct variations in total number density, plasma temperature, and electron number density, resulting in improved spectral stability. To ascertain the efficacy of the SS-PISF method, comprehensive experimental validation is conducted, comparing its performance against the full spectrum normalization method and the simplified spectral standardization method. Experimental tests on aluminum alloy samples demonstrate a remarkable improvement, with the determination coefficient (R²) value of calibration curves increasing by 25.307% after SS-PISF correction. Additionally, the root mean square error of validation (RMSE_V) and standard deviation (STD_V) experience a notable reduction by 28.374% and 2.323%, respectively. For alloy steel samples, the SS-PISF corrected calibration curves exhibit a 14.630% increase in R² and a 17.303% decrease in STD_V. Similarly, for ore pressed samples, the SS-PISF corrected calibration curves witness a 7.031% enhancement in R², alongside a decrease in RMSE_V and STD_V by 21.939% and 4.726%, respectively. These empirical results substantiate the efficacy of the SS-PISF method in significantly improving the spectral stability of LIBS. As a result, SS-PISF holds substantial promise for widespread application in laser-induced breakdown spectroscopy.

In the detection of aqueous solutions through Laser-induced Breakdown Spectroscopy (LIBS), the implementation of nebulization is instrumental in mitigating liquid splashing and preventing plasma quenching. However, gas dilution during nebulization would greatly undermine the detection limits. This study proposes a needle tip-enhancement method that excites the metal tip to produce a high-temperature plasma before ablating the nebulized solution. The optimization results for needle tip materials reveal that among the seven materials Zn, Ag, Cu, Mo, Ni, Ti, and W, Zn, Ag, and Cu exhibit minimal interference with the target elements Mn and Cr, with Zn showing the highest spectral enhancement effect. The average Pearson correlation between the enhancement effect and the melting and boiling points of the materials exceeds 0.90. Further optimization of the needle tip diameter revealed that a diameter that is too thin can cause material deformation, while a diameter that is too thick would lead to liquid adsorption and deep pit formation. Therefore, a diameter of 0.5 mm was chosen as the optimal under experimental conditions. Additionally, the optimization results of the relative position between the aerosol plume and the needle tip indicate that the best enhancement effect occurs when the aerosol plume passes through the core of the plasma. Using a needle tip-enhancement method, we detected Cu, Mn, and Cr in water samples, applying machine learning for quantitative analysis. The Gaussian Process Regression (GPR) model yielded the highest accuracy at 0.99. The spectral intensities of Cu, Mn, and Cr were enhanced by 7.20, 8.57, and 11.49 times, respectively. Detection limits of Cu, Mn, and Cr with needle tip-enhancement reached 42.80 mg L⁻¹, 51.25 mg L⁻¹, and 58.40 mg L⁻¹. This study has the potential to facilitate real-time online monitoring of water solutions.

The poor spectral stability of laser-induced breakdown spectroscopy (LIBS) seriously affects its analytical performance, which is a key obstacle to its further development. To overcome this challenge, an improved spectral standardization method based on plasma image-spectrum fusion (ISS-PISF) was proposed in this study. This method, for the first time, considers and quantifies the influence of plasma morphology on spectral intensity based on the line-integrated intensity formula of LIBS spectra. It recognizes that the spectral fluctuations mainly stem from variations in total number density, plasma temperature, electron number density, and plasma morphology. Therefore, ISS-PISF innovatively utilizes easily accessible features from plasma images and spectra to eliminate the influence of these four plasma parameters, thereby improving the spectral stability and analytical performance of LIBS. To validate the effectiveness of this method, the spectra of aluminum alloy samples obtained under complex detection conditions simulated by varying laser energy and defocusing amount were analyzed. After correction by ISS-PISF, the R² for Mg I 516.73 nm, Mn II 294.92 nm, and Si I 288.16 nm improved to 0.990, 0.976, and 0.961, and the average RMSE of the validation set decreased by 46.154%, while the average STD of the validation set decreased by 37.405%. These experimental results indicate that this study provides a simple, effective, and physically supported spectral standardization method, which contributes to the further promotion and application of LIBS.