Pixel-based Methods Research Articles

Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information

Pixel-based classification method for earthquake-induced landslide mapping using remotely sensed imagery, geospatial data and temporal change information

Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue

The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during sexual transmission. Quantification of immune cells in genital tissue is therefore an important outcome for studies on HIV susceptibility and prevention. Immunofluorescence microscopy allows for precise visualization of immune cells in mucosal tissues; however, this technique is limited in clinical studies by the lack of an accurate, unbiased, high-throughput image analysis method. Current pixel-based thresholding methods for cell counting struggle in tissue regions with high cell density and autofluorescence, both of which are common features in genital tissue. We describe a deep-learning approach using the publicly available StarDist method to count cells in immunofluorescence microscopy images of foreskin stained for nuclei, CD3, CD4, and CCR5. The accuracy of the model was comparable to manual counting (gold standard) and surpassed the capability of a previously described pixel-based cell counting method. We show that the performance of our deep-learning model is robust in tissue regions with high cell density and high autofluorescence. Moreover, we show that this deep-learning analysis method is both easy to implement and to adapt for the identification of other cell types in genital mucosal tissue.

Automatisation of bone age calculation using a pixel-based method and a deep learning network

Automatisation of bone age calculation using a pixel-based method and a deep learning network

Attenuation Tomography Using Low-frequency Ultrasound with Variational Autoencoder for Thorax Imaging: Experimental Study.

In this paper, we introduce a novel inversion methodology employing the variational autoencoder (VAE) for human thorax attenuation tomography using low-frequency ultrasound. The VAE is trained to assimilate the structural priors of the human thorax, utilizing training samples generated from computed tomography (CT) scans. This approach enables the compression of high-dimensional attenuation distributions into a lowerdimensional latent space. During the inversion process, the latent code is optimized, and then the reconstructed model is generated by the decoder of the VAE. This process can effectively integrate prior information of the domain of interest (DOI) into the inversion through coding and decoding, which would mitigate the ill-posedness of the inverse problem and facilitate better outcomes. Our method demonstrates robust generalization capabilities and noise resilience in numerical simulations, outperforming the conventional pixel-based Gauss-Newton method. Human subject experiment further corroborates the effectiveness of our approach. This is also the first experimental validation of the feasibility of low-frequency ultrasound functional imaging of the human thorax. Although the current study presents certain limitations, it underscores the potential of low-frequency ultrasound in the continuous monitoring of the human respiratory system.

Sentinel-2 image based smallholder crops classification and accuracy assessment by UAV data

Timely and accurate extraction of crop planting units plays a critical role in crop yield estimation, soil management, food supplies, and disaster warnings. However, precisely mapping crop types is challenging in smallholder farming systems due to heterogeneous and mixed pixels, where field sizes are small, and crop types are very diverse. In this paper, the crop type system of Huocheng County in northwest China as an example area, crop classification feature variables are constructed using Sentinel-2 remote sensing images, and combining the ReliefF algorithm, three feature selection classification models are established by 3092 crop type sampling points data. The crop planting units are extracted using pixel-based and object-based classification methods, respectively, and unmanned aerial vehicle (UAV) data assess the accuracy as ground truth. The Sentinel-2 image based smallholder crops classification results indicate that: The effective combination of optimal input feature variables selection and classification models significantly improves crop classification accuracy. This is particularly evident when integrating this approach into pixel-based classification, addressing challenges such as low pixel classification accuracy in regions of this type. The validation results from UAV data also confirm these findings. At validation points 2, 3, and 4, pixel-based classification demonstrates significantly higher accuracy compared to object-based classification, with pixel-based classification accuracy improving by 9.16%, 7.83%, and 31.83%, respectively. Although the UAV validation accuracy does not reach a high level, pixel-based classification remains the optimal choice for smallholder crop classification. This method offers new insights and references for research related to the classification of smallholder crops with complex planting structures.

The Basic Concept and Application Status of Laser Induced Forward Transfer

Laser Induced Forward Transfer (LIFT) is a new type of transfer method. It uses high-energy laser as an energy source to irradiate solid or liquid materials, allowing them to be emitted onto the receiving substrate under the force of steam to achieve the transfer of material. Nowadays, although most of the work on LIFT is still in the laboratory stage, compared to the traditional process of LIFT, its nozzle free characteristics, pixel-based transfer method, make it have high production efficiency, universality of materials, and compatibility with Computer numerical control (CNC) machine tools. In this review, the fundamental principle of LIFT is introduced and the great potential of LIFT in many fields is presented and analyzed, including Three-dimensional (3D) bioprinting, industrial manufacturing, energy storage material manufacturing, new-type energy and so on. The conclusion is that if the application of LIFT in the manufacturing field can be achieved, the efficiency and flexibility of modern digital production will be greatly improved. The LIFT method has great development prospects in the manufacturing field.

Spatial and temporal evolutions of terrestrial vegetation drought and the influence of atmospheric circulation factors across the Mainland China

Vegetation drought can reduce the net ecosystem productivity, resulting in disturbances to the sustainable development of humans and nature. In this study, Vegetation Health Index (VHI) was used to identify vegetation drought. The spatial and temporal evolution of vegetation drought was characterized using the Pixel-based Trend Identification Method (PTIM) in China from 1982 to 2020. Moreover, multiple cross-wavelet techniques were used for revealing the correlations between atmospheric circulation factors and their combinations with vegetation drought. The results indicates that: (1) there is a decreasing trend of vegetation drought in China during the study period, while it will show an increasing trend in the future; (2) the South China is prone to spring drought and summer drought, and the Sichuan Basin is prone to autumn drought and winter drought; (3) in spring, the most obvious trend of vegetation drought mitigation appears; (4) a combination of atmospheric circulation factors, North Pacific Index (NPI)-North Atlantic Oscillation (NAO)-Arctic Oscillation (AO), has the most significant impact on vegetation drought across China.

Automatic building footprint extraction from photogrammetric and LiDAR point clouds using a novel improved-Octree approach

Extracting building footprints from optical data is a time-consuming process. Automatic extraction of building footprints from point clouds is a challenging problem in terms of geometric irregularities, noisy points, points density, and accuracy. The aim of this paper is to automatically extract and regularize building footprints using point clouds with a new approach called Improved Octree (I-Octree) by modifying the Octree method. The method consists of the separation of ground and above ground objects from the point cloud by Simple Morphological Filter (SMRF), the removing noisy points from point cloud with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, the automatic extraction of building footprints by I-Octree, and the regularization of the building footprints with Automatic Building Outline Regularization (ABORE) method. The proposed approach was implemented on photogrammetric and Light Detection and Ranging (LiDAR) in four test areas. Ground truth maps were utilized as reference data for accuracy analysis by using pixel-based accuracy method. The accuracy results were above 90 % for the photogrammetric point clouds and above 97 % for the LiDAR point cloud. It was proven that the proposed approach can extract and regularize the selected buildings with high accuracy compared the studies in literature. In conclusion, it was demonstrated that the proposed approach enables the automatic extraction and regularization of building footprints from point clouds. Consequently, the map production process with point cloud data is facilitated to be both more efficient and rapid, and the results confirm the high efficacy of the proposed approach.

Learning from prior geological information for geotechnical soil stratification with tree-based methods

Geotechnical subsurface stratification based on sparse measurements presents a significant challenge. Learning from prior geological information, such as learning soil layer distribution patterns from stratification results (2D images) of adjacent data-rich projects, is an emerging approach to reducing uncertainties caused by data scarcity. Existing methods rely on pixel-based techniques that require training images to have identical soil types as the testing image. Additionally, pixel-based methods are prone to error accumulation. To address these issues, this study introduces a new framework that focuses solely on learning boundary information from training image rather than soil types. This eliminates the need for matching soil types between training and testing images. Tree-based model is proposed to learn boundary information from training images. Contrary to conventional tree-based models that use coordinates as input, this study employs a set of designed distance fields (boundary dictionary) to represent complex boundary patterns. A selection process is introduced to identify the most important distance fields from the training images. Using these selected distance fields for model input results in soil stratification that aligns well with both borehole data and training image boundaries. The proposed method's efficacy is validated through multiple simulated and real-world cases. The proposed method outperforms pixel-based methods in multiple cases, achieving up to a 25% improvement in accuracy. This method is robust and it yields consistent results for a variety of training images considered. Additionally, the proposed method also provides quantification of interpolation uncertainty through the Gini impurity method.

Dynamic Characteristics of Meteorological Drought and Its Impact on Vegetation in an Arid and Semi-Arid Region

Under the background of global climate warming, meteorological drought disasters have become increasingly frequent. Different vegetation types exhibit varying responses to drought, thus, exploring the heterogeneity of the impact of meteorological drought on vegetation is particularly important. In this study, we focused on Inner Mongolia (IM) as the research area and employed Standardized Precipitation Evapotranspiration Index (SPEI) and Vegetation Health Index (VHI) as meteorological drought and vegetation indices, respectively. The Breaks for Additive Seasons and Trend algorithm (BFAST) was utilized to reveal the dynamic characteristics of both meteorological drought and vegetation changes. Additionally, the Pixel-Based Trend Identification Method (PTIM) was employed to identify the trends of meteorological drought and vegetation during spring, summer, autumn, and the growing season. Subsequently, we analyzed the correlation between meteorological drought and vegetation growth. Finally, the response of vegetation growth to various climate factors was explored using the standardized multivariate linear regression method. The results indicated that: (1) During the study period, both SPEI and VHI exhibited a type of interrupted decrease. The meteorological drought was aggravated and the vegetation growth was decreased. (2) Deserts and grasslands exhibited higher sensitivity to meteorological drought compared to forests. The strongest correlation between SPEI-3 and VHI was observed in desert and grassland regions. In forest areas, the strongest correlation was found between SPEI-6 and VHI. (3) The r between severity of meteorological drought and status of vegetation growth was 0.898 (p < 0.01). Vegetation exhibits a more pronounced response to short-term meteorological drought events. (4) Evapotranspiration is the primary climatic driving factor in the IM. The findings of this study provide valuable insights for the rational utilization of water resources, the formulation of effective irrigation and replenishment policies, and the mitigation of the adverse impacts of meteorological drought disasters on vegetation growth in the IM.

Spatiotemporal imagery selection for full coverage image generation over a large area with HFA-Net based quality grading

ABSTRACT Remote sensing images often need to be merged into a larger mosaic image to support analysis on large areas in many applications. However, the performance of the mosaic imagery may be severely restricted if there are many areas with cloud coverage or if these images used for merging have a long-time span. Therefore, this paper proposes a method of image selection for full coverage image (i.e. a mosaic image with no cloud-contaminated pixels) generation. Specifically, a novel High-Frequency-Aware (HFA)-Net based on Swin-Transformer for region quality grading is presented to provide a data basis for image selection. Spatiotemporal constraints are presented to optimize the image selection. In the temporal dimension, the shortest-time-span constraint shortens the time span of the selected images, obviously improving the timeliness of the image selection results (i.e. with a shorter time span). In the spatial dimension, a spatial continuity constraint is proposed to select data with better quality and larger area, thus improving the radiometric continuity of the results. Experiments on the GF-1 images indicate that the proposed method reduces the averages by 76.1% and 38.7% in terms of the shortest time span compared to the Improved Coverage-oriented Retrieval algorithm (MICR) and Retrieval Method based on Grid Compensation (RMGC) methods, respectively. Moreover, the proposed method also reduces the residual cloud amount by an average of 91.2%, 89.8%, and 83.4% when compared to the MICR, RMGC, and Pixel-based Time-series Synthesis Method (PTSM) methods, respectively.

Molecular fingerprints and health risks of smoke from home-use incense burning

Abstract. The burning of incense for home use is a widespread practice that has been shown to have significant negative impacts on human health and air quality. However, there is a lack of understanding regarding its emission profiles and associated health risks. To address this knowledge gap, we utilized a state-of-the-art thermal-desorption comprehensive two-dimensional gas chromatography–mass spectrometer (TD-GC × GC-MS) to (semi-)quantify the emission factors (EFs) of 317 volatile compounds and thoroughly investigate the organic profiles of smoke from incense burning across a full-volatility range. Results showed that toluene (70.8±35.7 µg g−1) is the most abundant compound in smoke from incensing burning, followed by benzene, furfural, and phenol. Phenol, toluene, furfural, 2-furanmethanol, benzene, and benzyl alcohol are the main contributors to ozone and secondary organic aerosol (SOA) estimation. Intermediate volatility organic compounds (IVOCs) accounted for 19.2 % of the total EFs but 40.0 % of the estimated SOA. Additionally, a novel pixel-based method, combined with aroma analysis, revealed that furfural can act as a key tracer of incense burning and is responsible for the distinctive aroma of incense smoke. High-bioaccumulation-potential (BAP) assessment using pixel-based partition coefficient estimation revealed that acenaphthylene, dibenzofuran, and phthalate esters (PAEs) are chemicals of high-risk concern and warrant further control. Our results highlight the critical importance of investigating home-use incense burning and provide new insights into the health impacts of smoke from incense burning using novel approaches.

Impact of segmentation algorithms on multisensor LULC classification in a semiarid Mediterranean area

Geographic object-based image analysis methods usually provide better results than pixel-based methods for classifying land use and land cover from high and medium resolution satellite imagery. This study compares the results of Random Forest (RF) and Multilayer Perceptron (MLP) when used to classify the segments obtained on an RGB+NIR Sentinel-2 image using three different segmentation algorithms, Multiresolution (MR), Region Growing (RG), and Mean-Shift (MS). The hyperparameters of these algorithms were optimised minimising the intra-object heterogeneity and maximizing the inter-object heterogeneity, integrating them in an optimization loop. Geometric and two different centrality and dispersion statistics were computed from some Sentinel-1, Sentinel-2 and LiDAR variables over the segments, and used as features to classify the datasets. The highest segment cross-validation accuracies were obtained with RF using MR segments: 0.9048 (k=0.8905), while the highest accuracies calculated with test pixels were obtained with MLP using MR segments: 0.9447 (k=0.9303), both with the mean and standard deviation of the feature set. Although the overall accuracy is quite high, there are problems with some classes in the confusion matrix and, significant misclassification appear when a qualitative analysis of the final maps is performed, indicating that the accuracy metrics may be overestimated and that a qualitative analysis of the results may also be necessary.

Residual shape adaptive dense-nested Unet: Redesign the long lateral skip connections for metal surface tiny defect inspection

The state-of-the-art metal surface defect inspection methods have two problems: (1) they are sensitive to tiny defects because of their extreme small sizes, and (2) they cannot accurately locate the random appeared defects whose semantic relationship with the background context is weak. To solve these problems, Residual Shape Adaptive Dense-nested Unet, a pixel-based defect inspection method is proposed, to obtain the exact shape and location of the defect, by (1) assembling different depth Unet branches with dense skip connections as the feature extractor to combine multi-semantic level visual features; (2) adding Residual Shape Adaptive modules on the dense skip connections to help the model locate the defect regions; and (3) introducing the multi-branch training method which enables model pruning to reduce redundant parameters and accelerate the inspection speed. Experiments are conducted and demonstrated that the Residual Shape Adaptive Dense-nested Unet achieves the best performance among the state-of-the-art defect inspection methods.

Crack inspection in tunnel structures by fusing information from a 3D light detection and ranging and pan-tilt-zoom camera system

This study proposes a practical crack inspection method for tunnel structures by fusing measurements from two novel sensors: 3D light detection and ranging (LiDAR) and pan-tilt-zoom (PTZ) camera. The proposed method comprises three phases: prerequisite phase, real-time measurement phase, and inspection phase. First, the 3D LiDAR and the PTZ camera is combined to generate 3D point cloud data (PCD) with RGB data prior to extract the plane contained the crack. Second, the PTZ camera tracked the crack of interest at the pre-registered location. Specifically, several signal processing methods including the Hough transform and random sample consensus are addressed to effectively recognize the crack of interest. Then, the pan and tilt of the camera are controlled to track the crack of interest and record the crack at several magnifications of the zoom automatically because the optimal magnification value of the zoom is unknown in practical field applications. Third, crack propagation rate is estimated by calculating the length of the crack using information on the crack recorded in previous measurements. Cracks in a recorded image are extracted by addressing a multiscale multilevel mask deep convolutional neural network (MSML Mask DCNN) and pixel-based clustering method. The MSML Mask DCNN constructs distinct feature maps to effectively extract features of cracks and the pixel-based clustering method effectively eliminate cracks not of interest to accurately calculate the crack propagation rate for the crack of interest. Finally, the crack propagation rate was calculated through statistical analysis with several images at different zoom magnifications to secure high accuracy. Field experiments demonstrate that the proposed method is effective to inspect cracks. Systematic analysis on experiments also revealed that all methods deployed in the crack inspection enable ensures high accuracy and robustness in field applications. Consequently, the proposed crack inspection system and method provide a cost-effective and autonomous solution for crack inspection of tunnel structures.

Comparison of Automatic Classification Methods for Identification of Ice Surfaces from Unmanned-Aerial-Vehicle-Borne RGB Imagery

This article describes a comparison of the pixel-based classification methods used to distinguish ice from other land cover types. The article focuses on processing RGB imagery, as these are very easy to obtained. The imagery was taken using UAVs and has a very high spatial resolution. Classical classification methods (ISODATA and Maximum Likelihood) and more modern approaches (support vector machines, random forests, deep learning) have been compared for image data classifications. Input datasets were created from two distinct areas: The Pond Skříň and the Baroch Nature Reserve. The images were classified into two classes: ice and all other land cover types. The accuracy of each classification was verified using a Cohen’s Kappa coefficient, with reference values obtained via manual surface identification. Deep learning and Maximum Likelihood were the best classifiers, with a classification accuracy of over 92% in the first area of interest. On average, the support vector machine was the best classifier for both areas of interest. A comparison of the selected methods, which were applied to highly detailed RGB images obtained with UAVs, demonstrates the potential of their utilization compared to imagery obtained using satellites or aerial technologies for remote sensing.

Breast Cancer Segmentation from Ultrasound Images Using ResNext-based U-Net Model

Breast cancer is a type of cancer caused by the uncontrolled growth and proliferation of cells in the breast tissue. Differentiating between benign and malignant tumors is critical in the detection and treatment of breast cancer. Traditional methods of cancer detection by manual analysis of radiological images are time-consuming and error-prone due to human factors. Modern approaches based on image classifier deep learning models provide significant results in disease detection, but are not suitable for clinical use due to their black-box structure. This paper presents a semantic segmentation method for breast cancer detection from ultrasound images. First, an ultrasound image of any resolution is divided into 256×256 pixel patches by passing it through an image cropping function. These patches are sequentially numbered and given as input to the model. Features are extracted from the 256×256 pixel patches with pre-trained ResNext models placed in the encoder network of the U-Net model. These features are processed in the default decoder network of the U-Net model and estimated at the output with three different pixel values: benign tumor areas (1), malignant tumor areas (2) and background areas (0). The prediction masks obtained at the output of the decoder network are combined sequentially to obtain the final prediction mask. The proposed method is validated on a publicly available dataset of 780 ultrasound images of female patients. The ResNext-based U-Net model achieved 73.17% intersection over union (IoU) and 83.42% dice coefficient (DC) on the test images. ResNext-based U-Net models perform better than the default U-Net model. Experts could use the proposed pixel-based segmentation method for breast cancer diagnosis and monitoring.

An advanced coverage estimation method to quantify biological soil crust coverage using Sentinel-2 imagery in desert and sandy land of China

ABSTRACT Monitoring the distribution and area change of biological soil crusts (BSCs) can enhance our understanding of the interactions between nonvascular plants and the environment in drylands. However, using only pixel-based binary classification methods results in large-area estimation errors at large scales. The lack of available calculation methods for directly measuring BSC coverage using multispectral satellite images makes it challenging to obtain BSC area data for further studies at large scales. To address these issues, this study developed feature space conceptual models for desert and sandy land based on the characteristics of BSC in drylands. The desert feature space comprised the normalized difference vegetation index (NDVI) combined with the brightness index (BI), encompassing moss, lichen, and non-BSC. The sandy land feature space relied on the biological soil crust index (BSCI) and the NDVI, including vegetation, mixed BSCs and sandy soil. Using Sentinel-2 satellite imagery and a spectral unmixing model, the abundance of BSCs was quantified in four BSC growth areas located in the Gurbantunggut Desert and Mu Us Sandy Land of China. Validation of the method indicated that the root mean square error (RMSE) of the BSC coverage estimation results was 10% and 8% in desert and sandy land, respectively (estimation accuracies of 79% and 81%, respectively). This demonstrated that the proposed method can effectively estimate BSC coverage at a subpixel scale. The resulting BSC coverage data can provide the possibility to evaluate the functions of regional ecosystems.

Lymphoma Cell Nuclei Classification using Color and Morphology Features

Abstract Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of Non-Hodgkin’s Lymphoma, presenting a great challenge for treatment due to its highly heterogeneous nature. DLBCL is diagnosed based on microscopy images of patient tissue samples. To help gain a better understanding of DLBCL, we developed an automated computer vision method to analyze morphological and color-based information within patient biopsies. We analyzed a dataset of whole slide images of DLBCL by segmenting individual cells and representing cell morphologies through a set of engineered features. The features were evaluated using a variety of visualization and machine learning (ML) classification techniques. Current state-of-the-art deep learning methods use images as the input in classification tasks achieving high performance but lacking in interpretability. A big challenge lies in finding out what features the pixel-based deep learning methods utilize in prediction. Here, we present a technique that not only yields high prediction accuracy but also provides insights into which of the features are key for prediction. We show that the color-based features have the highest importance for cell classification, allowing for the accurate identification of various cell types with an accuracy of 84% in a multi-class and 91% in a binary classification. Our results provide valuable insights for exploring cell image datasets to gain an in-depth view of the tumor microenvironment.

Mapping gully affected areas by using Sentinel 2 imagery and digital elevation model based on the Google Earth Engine

Gullies pose a great threat to the sustainable development of agricultural production and the ecological environment. Accurate mapping of gully affected areas plays an important role in regional environmental monitoring and management. In this paper, an object-based image analysis method based on the Google Earth Engine (OBIA-GEE) is proposed for mapping gully affected areas by using Sentinel 2 imagery and AW3D30 DEM data. The method utilizes the simple non-iterative clustering (SNIC) algorithm to segment the top-five principal component images derived from Sentinel-2 surface reflectance images. Then, the random forest (RF) algorithm is used for feature importance analysis and gully affected area mapping. Finally, the gully affected area mapping results are further optimized by establishing terrain skeleton lines. The proposed method is applied to five study areas with different landform types on the Chinese Loess Plateau, and the results show that the method achieves good performance in mapping gully affected areas, with an overall accuracy (OA) of 86.44%, an F score (F1) of 0.84, a user’s accuracy (UA) of 84.97%, and a producer’s accuracy (PA) of 83.90%. Compared with the OA of the results of a previous study (OA was 78.8%), the accuracy of the gully affected area mapping results herein improved by 7.64%. The RF feature importance results and findings of different feature combination cases demonstrate that textural information, time series spectral information, and topographic factors are important for mapping gully affected areas. Object-based and pixel-based classification results are compared, and the performance of the OBIA-GEE method is better than that of the pixel-based RF method. In addition, the OBIA-GEE method is highly efficient, which provides the possibility of applying the proposed method in large-scale studies. This research is beneficial to studies related to monitoring gullies and managing soil erosion.