Rapeseed seedling growth monitoring indicates growth status and detects problems, such as seedling gaps, seedbed unevenness, and diseases or insect pests in time, which play an important role in improving sowing strategies, promoting the decision-making of fertilizer prescription, and increasing economic efficiency. To improve the accuracy of rapeseed seedling growth assessment, a multi-growth stage growth assessment method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and semantic segmentation was proposed to assess the growth of rapeseed into excellent, average, and poor growth. First, to address the problem of complex field scenes and densely planted rapeseed leading to difficult segmentation of rapeseed seedlings and field drains, the original Deeplabv3+ model was improved by selecting the lightweight network MobileNetV2 as the backbone feature extraction network and fusing the coordinate attention(CA)module, which enables the model to better noise removal and feature extraction and improves the model's accuracy and robustness. Then, a field drain optimal centerline algorithm is proposed to obtain the optimal centerline of all field drain in the image and determine the field box position. Finally, eight growth-related feature values for rapeseed seedling were constructed, and were used as feature vectors in a random forest (RF) to construct multi-growth stage growth assessment model for rapeseed seedlings. The results indicate that the improved DeeplabV3+ network outperformed the original DeeplabV3+ network, with the mean pixel accuracy increasing from 78.43 % to 87.47 % (an improvement of 9.04 %) and the average intersection over union (mIoU) increasing from 67.45 % to 76.89 % (an improvement of 9.44 %). The mean positional deviation of the centerline was -5.29 pixels with a standard deviation of 9.51 and a mean angular deviation of -0.01848 rad with a standard deviation of 0.00791, which can effectively detect the centerline of the field drain. The precision, sensitivity, specificity, and accuracy of the proposed method were 96.35 %, 96.34 %, 97.20 %, and 96.34 %, respectively. The algorithm of this study can efficiently segment rapeseed seedlings and field drains, obtain the optimal centerline of the field drain, and be used for rapeseed seedling multi-growth stage growth monitoring, which provides a theoretical basis and technical reference for rapeseed seedling multi-growth stage growth monitoring.

The ecological quality of large-scale farms is a critical determinant of crop growth. In this paper, an ecological assessment procedure suitable for agricultural regions should be developed based on an improved remote sensing ecological index (IRSEI), which introduces an integrated salinity index (ISI) tailored to the salinized soil characteristics in farming areas and incorporates ecological indices such as the greenness index (NDVI), the humidity index (WET), the dryness index (NDBSI), and the heat index (LST). The results indicate that between 2013 and 2022, the mean IRSEI increasing from 0.500 in 2013 to 0.826 in 2020 before decreasing to 0.646 in 2022. From 2013 to 2022, the area of the farm that experienced slight to significant improvements in ecological quality reached 1419.91 km2, accounting for 71.94% of the total farm area. An analysis of different land cover types revealed that the IRSEI performed more reliably than did the original RSEI method. Correlation analysis based on crop yields showed that the IRSEI method was more strongly correlated with yield than was the RSEI method. Therefore, the proposed IRSEI method offers a rapid and effective new means of monitoring ecological quality for agricultural planting areas characterized by soil salinization, and it is more effective than the traditional RSEI method.

Soil datasets with outliers lead to inaccurate farm-level digital soil mapping (DSM) results. Existing methods identify potential outliers in soil datasets based on expert experience or simple statistics that neglect the geographical characteristics of soil. In this paper, a novel potential outlier recognition method was developed from the perspective of geographical context. First, spatial search distance was automatically determined by the spatial distance among soil samples. Second, similarities of adjacent soil samples and the local spatial variation level were comprehensively considered to calculate outlier scores. Finally, a frequency histogram of outlier scores was generated to determine a suitable threshold for recognizing potential abnormal samples. To validate the proposed method, it was compared to Lambda and Box-Plot methods, and the ordinary kriging method was used to map five soil properties, including pH, soil organic matter, total nitrogen, available phosphorus and available potassium, in an agricultural region. Then, a synthetic study using artificially contaminated DEM data was also conducted. The comparative experiment shows that the proposed method is better able to recognize potential outliers by mining the local spatial structure, as indicated by lower mean absolute error (MAE) and root mean square error (RMSE) values. It can be concluded that consideration of local spatial autocorrelation and heterogeneity is helpful in recognizing potential outliers.

Sampling design plays a critical role in farm-level digital soil mapping (DSM). In many cases, a soil mapping model may not have been decided upon at the sample design stage. Design-based sampling may be more appropriate than model-based sampling because it is independent of subsequent soil mapping models. However, existing sampling methods optimize the sample size and locations in geographical space or feature space without considering the impacts of environmental similarity in local geographical space. In this paper, a novel sampling design method based on local environmental similarity was developed. Image segmentation was introduced into the sampling design by partitioning agricultural soil into subregions with good spatial continuity, within-region homogeneity, and between-region heterogeneity to determine the optimal sample size and locations. First, the environmental similarity between adjacent soils was calculated. Second, the merging process was iteratively conducted, and a series of segmentations was generated. Finally, the optimal sample size and locations were determined based on the optimal segmentation results. To validate the proposed method, it was compared with stratified random sampling, k -means sampling, and spatially balanced sampling methods. Two mapping models, ordinary kriging and sandwich estimation, were employed to map five soil properties, including pH, soil organic matter, total nitrogen, available phosphorus, and available potassium. These comparative experiments showed that the proposed method had better potential to generate farm-level muti-soil property mapping results with good accuracy than the competing sampling methods. In conclusion, consideration of local environmental similarity and the use of image segmentation for soil sampling were helpful in determining the optimal sample size and key sample locations.

The identification of viruses from negative staining transmission electron microscopy (TEM) images has mainly depended on experienced experts. Recent advances in artificial intelligence have enabled virus recognition using deep learning techniques. However, most of the existing methods only perform virus classification or semantic segmentation, and few studies have addressed the challenge of virus instance segmentation in TEM images. In this paper, we focus on the instance segmentation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and other respiratory viruses and provide experts with more effective information about viruses. We propose an effective virus instance segmentation network based on the You Only Look At CoefficienTs backbone, which integrates the Swin Transformer, dense connections and the coordinate-spatial attention mechanism, to identify SARS-CoV-2, H1N1 influenza virus, respiratory syncytial virus, Herpes simplex virus-1, Human adenovirus type 5 and Vaccinia virus. We also provide a public TEM virus dataset and conduct extensive comparative experiments. Our method achieves a mean average precision score of 83.8 and F1 score of 0.920, outperforming other state-of-the-art instance segmentation algorithms. The proposed automated method provides virologists with an effective approach for recognizing and identifying SARS-CoV-2 and assisting in the diagnosis of viruses. Our dataset and code are accessible at https://github.com/xiaochiHNU/Virus-Instance-Segmentation-Transformer-Network.

Sliding-window-based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates applications such as multisource or multiscale data fusion, data assimilation, and product uncertainty quantification since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise uncertainty quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of handling uncertainty in HSI patch mixing caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the uncertainty of the observed HSI and provides the uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10%–20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form uncertainty quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive.

With the development of medium- and high-resolution satellites, successfully segmenting differently sized geo-objects remains a challenging issue for geographic object-based image analysis (GEOBIA). The hybrid image segmentation method is a good alternative to produce good segmentation that best matched the different sizes of geo-objects. However, the existing methods almost use segmentation parameters (SPs), such as scale, to control the sizes and shapes of segments. This will lead to two issues: (1) one single scale is impossible to segment every geo-object well due to the land cover complexity within remote-sensing imageries; (2) over- and under-segmented regions still occur in the segmentation results, whatever using any advanced segmentation methods. To solve the above problems, this paper developed a hybrid image segmentation method with local scale-guided hierarchical region merging and further over- and under-segmentation processing. First, the primitive segmentation was produced and then stratified into layers with different land covers. Then, the local scale was calculated for a more objective merging process in the separating layers. Third, the over- and under-segmentation at separating layers was recognized and re-processed for achieving a fine segmentation. To validate the proposed method, it was applied to three test images of gaofen-1 satellite with different land cover types, and ten competing methods were compared. The visual and quantitative results indicated the advantage of our method in segmenting out different sizes of geo-objects, which can effectively reduce the over- and under-segmentation error.

Soil sampling is critical to obtaining reliable input for farm field-level digital soil mapping (DSM). Sample size and location are the key issues for soil sampling. However, sample size is often restricted by available budgets. In this case, recognizing the key sample locations is necessary. Existing methods have optimized the sample locations in a global manner without considering the impacts of local heterogeneity of soil. In this paper, a novel sampling approach based on the local heterogeneity of soil with a limited sample size (40 samples in this research) was developed. First, the local heterogeneity of soil was inferred. Second, the sub-regions were divided based on the level of local soil heterogeneity and the corresponding sample numbers were determined. Finally, the key sample locations were determined based on the fuzzy memberships. To validate the proposed method, it was compared with stratified random sampling, k-means sampling and conditional Latin hypercube sampling. The ordinary kriging method was applied to map five soil properties, including soil organic matter, pH, total nitrogen, available phosphorus and available potassium. The comparative experiments showed that the proposed method has better robustness in satisfying good mapping accuracy for multi-soil properties at the farm field level compared with the competing sampling methods, as indicated by the relatively lower and more stable mean bias error (MBE) and root mean square error (RMSE) values. It can be concluded that the consideration of local heterogeneity of soil is helpful to recognize the key sample locations for limited sample sizes.

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

The unsupervised segmentation evaluation (USE) method has been commonly used for remote sensing segmentation parameter (SP) determinations to produce good segmentation results, due to its objectiveness and high efficiency.Existing studies have used different criteria to measure homogeneity and heterogeneity and have used certain combination strategies to form overall evaluations.However, different criteria have unique statistical characteristics.The differentiated statistical characteristics maintained in homogeneity and heterogeneity calculations may result in inherent instability in the USE results, leading to unsuitable SP selections.Moreover, few studies have focused on the simultaneous determination of a single optimal SP and multiple optimal SPs.In this article, double-variance (DV) measures were proposed for recognizing more suitable SPs.Then, two combination strategies, F-measure and local peak (LP), were applied to test the potential of using DV measures to determine a single SP and multiple SPs, respectively.The multiresolution segmentation algorithm and Gaofen-1 data were used to test the proposed method.The comparative results indicated that the DV is a more promising internal homogeneity and external heterogeneity metric for segmentation evaluation and optimal SP determination compared to conventional methods.The F-measure-based DV method could produce better overall goodness of segmentation for differently sized natural geoobjects, compared with the competing methods.The LP-based DV method could obtain multiple optimal scales that produced better segments for the identification of small, natural geo-objects to large, semantic geo-objects, compared to the competitive methods.

Airborne light detection and ranging (LiDAR) data are widely applied in building reconstruction, with studies reporting success in typical buildings. However, the reconstruction of curved buildings remains an open research problem. To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives. The input LiDAR point clouds are first converted into contours where individual buildings are identified. After recognizing geometric units (primitives) from building contours, we get initial models by matching the basic geometric primitives to these primitives. To polish assembly models, we employ a warping field for model refinements. Specifically, an embedded deformation (ED) graph is constructed via downsampling the initial model. Then, the point to model displacements is minimized by adjusting node parameters in the ED graph based on our objective function. The presented framework is validated on several highly curved buildings collected by various LiDAR in different cities. The experimental results, as well as accuracy comparison, demonstrate the advantage and effectiveness of our method. The new insight attributes to an efficient reconstruction manner. Moreover, we prove that the primitive-based framework significantly reduces the data storage to 10%-20% of classical mesh models.

Image segmentation is a key issue in geographic object-based image analysis, thus determining the appropriate segmentation parameter is a prerequisite to allowing for obtaining accurate segmentation. In this study, an unsupervised segmentation parameter selection method using the local spatial statistics was proposed for achieving the automatic parameter optimization of image segmentation. The two measure of within-segment homogeneity (WSH) and between-segment heterogeneity (BSH) were calculated using local spatial statistics approach, and then integrated into a global value for indicating the overall segmentation quality. In addition, the contribution of the common boundary between each segment and one of its neighboring segments was considered in BSH calculation for obtaining a more objective evaluation. For this experiment, the multi-resolution segmentation (MRS) method was used as a segmentation algorithm and GF-1 image used as test data. The measure analysis experiment of the proposed method showed BSH is more sensitive to under-segmentation. The visual and discrepancy measures results of the proposed method compared with the other four methods revealed that the proposed method is more potential to recognize the proper segmentation parameter with the purpose of allowing for obtaining segmentations with high quality.

Image segmentation technology, which can be used to completely partition a remote sensing image into non-overlapping regions in the image space, plays an indispensable role in high-resolution remote sensing image classification. Recently, the segmentation methods that combine segmenting with merging have attracted researchers’ attention. However, the existing methods ignore the fact that the same parameters must be applied to every segmented geo-object, and fail to consider the homogeneity between adjacent geo-objects. This paper develops an improved remote sensing image segmentation method to overcome this limitation. The proposed method is a hybrid method (split-and-merge). First, a watershed algorithm based on pre-processing is used to split the image to form initial segments. Second, the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation. For this experiment, we used GF-1 images with three spatial resolutions: 2 m, 8 m and 16 m. Six different test areas were chosen from the GF-1 images to demonstrate the effectiveness of the improved method, and the objective function (F (v, I)), intrasegment variance (v) and Moran’s index were used to evaluate the segmentation accuracy. The validation results indicated that the improved segmentation method produced satisfactory segmentation results for GF-1 images (average F (v, I) = 0.1064, v = 0.0428 and I = 0.17).

With the increasing requirements of precision agriculture for massive and various kinds of data, remote sensing technology has become indispensable in acquiring the necessary data for precision agriculture. Understanding the spatial variability of a target soil variable (i.e., soil mapping) is a critical issue in solving many agricultural problems. Field sampling is one of the most commonly used technologies for soil mapping, but sample sizes are restricted by resources, such as field labor, soil physicochemical analysis, and funding. In this paper, we proposed a sampling design method with both good spatial coverage and feature space coverage to achieve more precise spatial variability of farm field-level target soil variables for limited sample sizes. The proposed method used the super-grid to achieve good spatial coverage, and it took advantage of remote sensing products that were highly correlated with the target soil property (SOM content) to achieve good feature space coverage. For the experiments, we employed the ordinary kriging (OK) method to map the soil organic matter (SOM) content. The different sized super-grid comparison experiments showed that the 400 × 400 m2 super-grid had the highest SOM content mapping accuracy. Then, we compared the proposed method to regular grid sampling (good spatial coverage) and k-means sampling (good feature space coverage), and the experimental results indicated that the proposed method had greater potential in the selection of representative samples that could improve the SOM content mapping accuracy.

In minimal invasive surgery, it is important to rebuild and visualize the latest deformed shape of soft-tissue surfaces to mitigate tissue damages. This letter proposes an innovative Simultaneous localization and mapping (SLAM) algorithm for deformable dense reconstruction of surfaces using a sequence of images from a stereoscope. We introduce a warping field based on the embedded deformation nodes with three-dimensional (3-D) shapes recovered from consecutive pairs of stereo images. The warping field is estimated by deforming the last updated model to the current live model. Our SLAM system can incrementally build a live model by progressively fusing new observations with vivid accurate texture; estimate the deformed shape of unobserved region with the principle as-rigid-as-possible; show the consecutive shape of models; and estimate the current relative pose between the soft-tissue and the scope. In-vivo experiments with publicly available datasets demonstrate that the 3-D models can be incrementally built for different soft-tissues with different deformations from sequences of stereo images obtained by laparoscopes. Results show the potential clinical application of our SLAM system for providing surgeon useful shape and texture information in minimal invasive surgery.

This paper presents a novel RGB-D 3D reconstruction algorithm for the indoor environment. The method can produce globally-consistent 3D maps for potential GIS applications. As the consumer RGB-D camera provides a noisy depth image, the proposed algorithm decouples the rotation and translation for a more robust camera pose estimation, which makes full use of the information, but also prevents inaccuracies caused by noisy depth measurements. The uncertainty in the image depth is not only related to the camera device, but also the environment; hence, a novel uncertainty model for depth measurements was developed using Gaussian mixture applied to multi-windows. The plane features in the indoor environment contain valuable information about the global structure, which can guide the convergence of camera pose solutions, and plane and feature point constraints are incorporated in the proposed optimization framework. The proposed method was validated using publicly-available RGB-D benchmarks and obtained good quality trajectory and 3D models, which are difficult for traditional 3D reconstruction algorithms.