Earth’s ionosphere is an important medium for navigation, communication, and radio wave transmission. Total Electron Content (TEC) is a descriptive quantify for ionospheric research. However, the traditional empirical model could not fully consider the changes of TEC time series, the prediction accuracy level of TEC data performed not high. In this study, an improved Extreme Learning Machine (ELM) model is proposed for ionospheric TEC prediction. Improvements involved the use of Empirical Mode Decomposition (EMD) and a Fuzzy C-Means (FCM) clustering algorithm to pre-process data used as input to the ELM model. The proposed model fully uses the TEC data characteristics and expected to perform better prediction accuracy. TEC measurements provided by the Centre for Orbit Determination in Europe (CODE) were used to evaluate the performance of the improved ELM model in terms of prediction accuracy, applicable latitude, and the number of required training samples. Experimental results produced a Mean Relative Error (MRE) and a Root Mean Square Error (RMSE) of 8.5% and 1.39 TECU, respectively, outperforming the ELM algorithm (RMSE=2.33 TECU and MRE=17.1%). The improved ELM model exhibited particularly high prediction accuracy in mid-latitude regions, with a mean relative error of 7.6%. This value improved further as the number of available training data increased and when 20-doys data were trained, achieving a mean relative error of 4.9%. These results suggest the proposed model offers higher prediction accuracy than conventional algorithms.

In this study, the problem of bundle adjustment was revisited, and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment. The innovation points are reflected in the following aspects: ① The proposed algorithm is not dependent on the Schur complement, and the calculation process is simple and clear; ② The complexities of time and space tend to O(n) in the context of world point number is far greater than that of images and cameras, so the calculation magnitude and memory consumption can be reduced significantly; ③ The proposed algorithm can carry out self-calibration bundle adjustment in single-camera, multi-camera, and variable-camera modes; ④ Some measures are employed to improve the optimization effects. Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness, and it has a strong adaptability as well, because the optimized results are accurate and robust even if the initial values have large deviations from the truth. This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry, computer vision and robotics.

As a consumed and influential natural plant beverage, tea is widely planted in subtropical and tropical areas all over the world. Affected by (sub) tropical climate characteristics, the underlying surface of the tea distribution area is extremely complex, with a variety of vegetation types. In addition, tea distribution is scattered and fragmentized in most of China. Therefore, it is difficult to obtain accurate tea information based on coarse resolution remote sensing data and existing feature extraction methods. This study proposed a boundary-enhanced, object-oriented random forest method on the basis of high-resolution GF-2 and multi-temporal Sentinel-2 data. This method uses multispectral indexes, textures, vegetable indices, and variation characteristics of time-series NDVI from the multi-temporal Sentinel-2 imageries to obtain abundant features related to the growth of tea plantations. To reduce feature redundancy and computation time, the feature elimination algorithm based on Mean Decrease Accuracy (MDA) was used to generate the optimal feature set. Considering the serious boundary inconsistency problem caused by the complex and fragmented land cover types, high resolution GF-2 image was segmented based on the MultiResolution Segmentation (MRS) algorithm to assist the segmentation of Sentinel-2, which contributes to delineating meaningful objects and enhancing the reliability of the boundary for tea plantations. Finally, the object-oriented random forest method was utilized to extract the tea information based on the optimal feature combination in the Jingmai Mountain, Yunnan Province. The resulting tea plantation map had high accuracy, with a 95.38% overall accuracy and 0.91 kappa coefficient. We conclude that the proposed method is effective for mapping tea plantations in high heterogeneity mountainous areas and has the potential for mapping tea plantations in large areas.

A new prediction method based on the nonlinear autoregressive model is proposed to improve the accuracy of medium-term and long-term predictions of Satellite Clock Bias (SCB). Forecast experiments for three time periods were implemented based on the precision SCB published on the International GNSS Server (IGS) server. The results show that the medium-term and long-term prediction accuracy of the proposed approach is significantly better compared to other traditional models, with the training time being much shorter than the wavelet neural network model.

The extraction of water bodies is essential for monitoring water resources, ecosystem services and the hydrological cycle, so analyzing water bodies from remote sensing images is necessary. The water index is designed to highlight water bodies in remote sensing images. We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images. Firstly, we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction. Subsequently, we apply KT transformation, LBV transformation, AWEI_nsh, and HIS transformation to the preprocessed image to calculate a new water index. Then, we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately. Meanwhile, we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies. Finally, we combine small and large water bodies to get complete water bodies. Compared with other traditional methods, our method has apparent advantages in water extraction, particularly in the extraction of small water bodies.

A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method. The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline. Due to the differences in satellite sensors when producing images, subtle but inherent stripes can appear at the stitching positions between the sensors. These stitchingstripes cannot be eliminated by conventional relative radiometric calibration. The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation, classification and interpretation of remote sensing images. Therefore, a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper. First, the inconsistency in grayscales around stripes is eliminated with the statistical method. Second, the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality. Finally, the details of the images are highlighted by a new image enhancement method, which makes the whole image clearer. Comprehensive experiments are performed, and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.

Gravity Anomaly Correction (GAC) is a very important term in leveling data processing. In most cases, it is troublesome for field surveyors to measure gravity when leveling. In this paper, based on the complete Bouguer Gravity Anomaly(BGA) map of WGM2012, the feasibility of replacing in-situ gravity surveying in China is investigated. For leveling application, that is to evaluate the accuracy of WGM2012 in China. Because WGM2012 is organized with a standard rectangle grid, two interpolation methods, bilinear interpolating and Inverse Distance Weighted (IDW) interpolating, are proposed. Four sample areas in China, i.e., Hanzhong, Chengdu, Linzhi and Shantou, are selected to evaluate the systems bias and precision of WGM2012. Numerical results show the average system bias of WGM2012 BGA in west China is about -100.1mGal (1mGal=10^-5m/s²) and the standard deviation is about 30.7mGal. Tests in Shantou indicate the system bias in plain areas is about -130.4mGal and standard deviation is about 6.8mGal. All these experiments means the accuracy of WGM2012 is limited in high mountain areas of western China, but in plain areas, such as Shantou,WGM2012 BGA map is quite good for most leveling applications after calibrating the system bias.

Convolutional Neural Networks (CNNs) have recently attracted much attention in the ship detection from Synthetic Aperture Radar (SAR) images. However, compared with optical images, SAR ones are hard to understand. Moreover, due to the high similarity between the man-made targets near shore and inshore ships, the classical methods are unable to achieve effective detection of inshore ships. To mitigate the influence of onshore ship-like objects, this paper proposes an inshore ship detection method in SAR images by using hybrid features. Firstly, the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images. Then, a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water. On this basis, the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left. Finally, to verify the effectiveness of the proposed method, numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.