Pixel Classification Research Articles

Semantic Segmentation of Surface Cracks in Urban Comprehensive Pipe Galleries Based on Global Attention

Cracks inside urban underground comprehensive pipe galleries are small and their characteristics are not obvious. Due to low lighting and large shadow areas, the differentiation between the cracks and background in an image is low. Most current semantic segmentation methods focus on overall segmentation and have a large perceptual range. However, for urban underground comprehensive pipe gallery crack segmentation tasks, it is difficult to pay attention to the detailed features of local edges to obtain accurate segmentation results. A Global Attention Segmentation Network (GA-SegNet) is proposed in this paper. The GA-SegNet is designed to perform semantic segmentation by incorporating global attention mechanisms. In order to perform precise pixel classification in the image, a residual separable convolution attention model is employed in an encoder to extract features at multiple scales. A global attention upsample model (GAM) is utilized in a decoder to enhance the connection between shallow-level features and deep abstract features, which could increase the attention of the network towards small cracks. By employing a balanced loss function, the contribution of crack pixels is increased while reducing the focus on background pixels in the overall loss. This approach aims to improve the segmentation accuracy of cracks. The comparative experimental results with other classic models show that the GA SegNet model proposed in this study has better segmentation performance and multiple evaluation indicators, and has advantages in segmentation accuracy and efficiency.

Abstract TMP120: Contralesional Corticospinal Tract Reinnervates Cervical Spinal Anatomical Regions With Highest Pre-Lesion Synapse Density After Unilateral M1 Stroke in Mice

Introduction: Stroke leaves many patients with significant motor impairment. Following ischemic lesions of the rodent primary motor cortex, sprouting of the contralesional corticospinal tract (cCST) into the injured hemicord is associated with improved motor recovery. In adult mice, most corticospinal neurons project to spinal interneurons, and few synapse directly on alpha motor neurons. We examined the distribution of newly formed corticospinal synapses in the mouse spinal cord, using genetically encoded tracers, 3D microscopy, and a spinal atlas with machine learning-based automated classification and registration. Hypothesis: We tested the hypothesis that cCST neurons innervate homotopic regions in the injured hemicord after unilateral motor cortex stroke in adult mice. Method: We induced a photothrombotic motor cortex stroke or performed sham surgery (n=4 per group) in 8-11 week old male C57/B6 mice and administered a contralesional motor cortex injection of an anterograde adeno-associated virus expressing both membrane-targeted tdTomato and synaptically-targeted eGFP. Cervical spinal cords were subjected to volumetric imaging via serial two-photon tomography (TissueCyte 1000) to characterize synaptic and axonal density of cCST collaterals in whole cervical spinal cords in both stroke and sham mice. Unbiased, global quantification of axonal and synaptic density across the entire cervical cord was accomplished by the development of a custom automated image analysis pipeline, incorporating a novel 3D spinal cord reference volume, published spinal cord annotations comprising 47 distinct anatomical regions (SpinalJ), and machine learning based pixel classification. Results: We observed cCST synapses in the 6-week post-stroke injured hemicord innervating lamina IV & IX most densely at C7 & C8 (See Figure). Conclusions: Contralesional CST reinnervation of the denervated hemicord is targeted to both lamina and cervical level specific.

Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging.

Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models-DRSNet, CNN-Visual Transformer, and GCN-conducting a comprehensive analysis to evaluate the advantages and limitations of each model.

Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning.

Supervised and unsupervised machine learning algorithms are routinelyapplied to time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large-scale, single-pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so-called weakly supervised problems, where ground-truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual-stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial-spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy-regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof-of-concept exemplification using printed ink samples imaged by ToF-SIMS. A second application-oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial-spectral characteristics in various applications and contexts.

Improved Loss Function for Mass Segmentation in Mammography Images Using Density and Mass Size.

Mass segmentation is one of the fundamental tasks used when identifying breast cancer due to the comprehensive information it provides, including the location, size, and border of the masses. Despite significant improvement in the performance of the task, certain properties of the data, such as pixel class imbalance and the diverse appearance and sizes of masses, remain challenging. Recently, there has been a surge in articles proposing to address pixel class imbalance through the formulation of the loss function. While demonstrating an enhancement in performance, they mostly fail to address the problem comprehensively. In this paper, we propose a new perspective on the calculation of the loss that enables the binary segmentation loss to incorporate the sample-level information and region-level losses in a hybrid loss setting. We propose two variations of the loss to include mass size and density in the loss calculation. Also, we introduce a single loss variant using the idea of utilizing mass size and density to enhance focal loss. We tested the proposed method on benchmark datasets: CBIS-DDSM and INbreast. Our approach outperformed the baseline and state-of-the-art methods on both datasets.

Two‐dimensional medical image segmentation based on U‐shaped structure

AbstractWith rapid developments in convolutional neural networks for image processing, deep learning methods based on pixel classification have been extensively applied in medical image segmentation. One popular strategy for such tasks is the encoder‐decoder‐based U‐Net architecture and its variants. Most segmentation methods based on fully convolutional networks will cause the loss of spatial and contextual information due to continuous pooling operations or strided convolution when decreasing image resolution, and make less use of contextual information and global information under different receptive fields. To overcome this shortcoming, this paper proposes a novel structure called RAAU‐Net. In our proposed RAAU‐Net structure, which is a modified U‐shaped architecture, we aim to capture high‐level information while preserving spatial information and focusing on the regions of interest. RAAU‐Net comprises three main components: a feature encoder module that utilizes a pre‐trained ResNet‐18 model as a fixed feature extractor, a multi‐receptive field extraction module that we developed, and a feature decoder module. We have tested our method on several 2D medical image segmentation tasks such as retinal nerve, breast tumor, skin lesion, lung, gland, and polyp segmentation. All the indexes of the model reached the best in the dataset of skin lesions, in which Accuracy, Precision, IoU, Recall, and Dice Score were 3.26%, 5.42%, 9.92%, 6.52%, and 5.95% higher than UNet.

Contextual Learning in Fourier Complex Field for VHR Remote Sensing Images.

Very high-resolution (VHR) remote sensing (RS) image classification is the fundamental task for RS image analysis and understanding. Recently, Transformer-based models demonstrated outstanding potential for learning high-order contextual relationships from natural images with general resolution ( ≈ 224 × 224 pixels) and achieved remarkable results on general image classification tasks. However, the complexity of the naive Transformer grows quadratically with the increase in image size, which prevents Transformer-based models from VHR RS image ( ≥ 500 × 500 pixels) classification and other computationally expensive downstream tasks. To this end, we propose to decompose the expensive self-attention (SA) into real and imaginary parts via discrete Fourier transform (DFT) and, therefore, propose an efficient complex SA (CSA) mechanism. Benefiting from the conjugated symmetric property of DFT, CSA is capable to model the high-order contextual information with less than half computations of naive SA. To overcome the gradient explosion in Fourier complex field, we replace the Softmax function with the carefully designed Logmax function to normalize the attention map of CSA and stabilize the gradient propagation. By stacking various layers of CSA blocks, we propose the Fourier complex Transformer (FCT) model to learn global contextual information from VHR aerial images following the hierarchical manners. Universal experiments conducted on commonly used RS classification datasets demonstrate the effectiveness and efficiency of FCT, especially on VHR RS images. The source code of FCT will be available at https://github.com/Gao-xiyuan/FCT.

Machine-learning-guided quantitative delineation of cell morphological features and responses to nanomaterials.

Delineation of cell morphological features is essential to decipher cell responses to external stimuli like theranostic nanomaterials. Conventional methods rely on labeled approaches, such as fluorescence imaging and flow cytometry, to assess cell responses. Besides potentially perturbing cell structure and morphology, these approaches are relatively complex, time-consuming, expensive, and may not be compatible with downstream analysis involving live cells. Herein, leveraging label-free phase-contrast or brightfield microscopy imaging and machine learning, the delineation of different cell types, phenotypes, and states for monitoring live cell responses is reported. Notably, pixel classification based on a supervised random forest classifier is used to distinguish between cells and backgrounds from the microscopy images, followed by cell segmentation and morphological feature extraction. Quantitative analysis shows that most of the compared cell groups have distinguishable size and shape features. Principal component analysis and unsupervised k-means clustering of morphological features reveal the possible existence of heterogenous cell subpopulations and treatment responses among the seemingly homogenous cell groups. This shows the merit of the reported approach in complementing conventional techniques for cell analysis. It is anticipated that the demonstrated method will further aid the implementation of machine learning to streamline the analysis of cell morphology and responses for early disease diagnosis and treatment response monitoring.

Satellite-based landslide distribution mapping with the adoption of deep learning approach in the Kuantan River Basin, Pahang

Landslides are one of the major geological phenomena that is widespread across the globe and have caused destructive outcomes to human life and the overall economic system. Tedious work is required to conventionally collect all evidence of multiple sizes of landslide occurrences in such a huge, developing city, including the Kuantan River Basin (KRB). In fact, landslides are difficult to identify in remote areas, such as in thick and mountainous areas, if no aerial devices or sensor technology is provided at the incident area. Ironically, the landslide distribution map is a useful tool that helps in staging the landslide mitigation plan for landslide-prone areas. Thus, the objectives of this study are (i) to identify landslide events using deep learning and vegetation index approaches on optical satellite data; and (ii) to develop landslide distribution mapping in KRB using the best approach. Remotely sensed optical images of Landsat 8 OLI and Worldview-2 were used to map the landslide distribution and study the spectral pattern of the landslide area. Normalized Difference Vegetation Index (NDVI) were generated for two consecutive years, which is from the year 2022 to 2023. Spectral bands in red and infrared are used to generate the NDVI for visual interpretation of landslide occurrences. The deep learning based on Convolutional Neural Network (CNN) model were used for the pixel classification process. The main output of this study would be a landslide distribution map for the KRB area with high accuracy. The result has also been verified using drone monitoring at the incident sites, which was able to improve landslide detection in tropical environments. Landslide distribution maps accuracy was measured by using the ROC-AUC method, the map accuracy is 88.9%. This map should help the government and private sector plan for the city’s urban development and provide proper planning for geohazard mitigation. An accurate landslide distribution map could be a source of reference for the National Disaster Management Authority (NADMA) for a quick rescue during emergency.

Effective Semi-Supervised Medical Image Segmentation with Probabilistic Representations and Prototype Learning.

Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous literature. To address this issue, we propose a probabilistic prototype-based classifier that introduces uncertainty estimation into the entire pixel classification process, including probabilistic representation formulation, probabilistic pixel-prototype proximity matching, and distribution prototype update, leveraging principles from probability theory. By explicitly modeling data uncertainty at the pixel level, model robustness of our proposed framework to tricky pixels, such as ambiguous boundaries and noises, is greatly enhanced when compared to its deterministic counterpart and other uncertainty-aware strategy. Empirical evaluations on three publicly available datasets that exhibit severe boundary ambiguity show the superiority of our method over several competitors. Moreover, our method also demonstrates a stronger model robustness to simulated noisy data. Code is available at https://github.com/IsYuchenYuan/PPC.

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.

MIS-Net: A deep learning-based multi-class segmentation model for CT images.

The accuracy of traditional CT image segmentation algorithms is hindered by issues such as low contrast and high noise in the images. While numerous scholars have introduced deep learning-based CT image segmentation algorithms, they still face challenges, particularly in achieving high edge accuracy and addressing pixel classification errors. To tackle these issues, this study proposes the MIS-Net (Medical Images Segment Net) model, a deep learning-based approach. The MIS-Net model incorporates multi-scale atrous convolution into the encoding and decoding structure with symmetry, enabling the comprehensive extraction of multi-scale features from CT images. This enhancement aims to improve the accuracy of lung and liver edge segmentation. In the evaluation using the COVID-19 CT Lung and Infection Segmentation dataset, the left and right lung segmentation results demonstrate that MIS-Net achieves a Dice Similarity Coefficient (DSC) of 97.61. Similarly, in the Liver Tumor Segmentation Challenge 2017 public dataset, the DSC of MIS-Net reaches 98.78.

SegHSI: Semantic Segmentation of Hyperspectral Images with Limited Labeled Pixels.

Hyperspectral images (HSIs), with hundreds of narrow spectral bands, are increasingly used for ground object classification in remote sensing. However, many HSI classification models operate pixel-by-pixel, limiting the utilization of spatial information and resulting in increased inference time for the whole image. This paper proposes SegHSI, an effective and efficient end-to-end HSI segmentation model, alongside a novel training strategy. SegHSI adopts a head-free structure with cluster attention modules and spatial-aware feedforward networks (SA-FFN) for multiscale spatial encoding. Cluster attention encodes pixels through constructed clusters within the HSI, while SA-FFN integrates depth-wise convolution to enhance spatial context. Our training strategy utilizes a student-teacher model framework that combines labeled pixel class information with consistency learning on unlabeled pixels. Experiments on three public HSI datasets demonstrate that SegHSI not only surpasses other state-of-the-art models in segmentation accuracy but also achieves inference time at the scale of seconds, even reaching sub-second speeds for full-image classification. Code is available at https://github.com/huanliu233/SegHSI.

SARSNet—A Novel CNN Approach for SARWater Body Segmentation

This paper presents the SARSNet architecture, developed to address the growing challenges in Synthetic Aperture Radar (SAR) deep learning-based automatic water body extraction. Such a task is riddled with significant challenges, encompassing issues like cloud interference, scarcity of annotated dataset, and the intricacies associated with varied topography. Recent strides in Convolutional Neural Networks (CNNs) and multispectral segmentation techniques offer a promising avenue to address these predicaments. In our research, we propose a series of solutions to elevate the process of water body segmentation. Our proposed solutions span several domains, including image resolution enhancement, refined extraction techniques tailored for narrow water bodies, self-balancing of the class pixel level, and minority class-influenced loss function, all aimed at amplifying prediction precision and streamlining computational complexity inherent in deep neural networks. The framework of our approach includes the introduction of a multichannel Data-Fusion Register, the incorporation of a CNN-based Patch Adaptive Network augmentation method, and the integration of class pixel level balancing and the Tversky loss function. We evaluated the performance of the model using the Sentinel-1 SAR electromagnetic signal dataset from the Earth flood water body extraction competition organized by the artificial intelligence department of Microsoft. In our analysis, our suggested SARSNet was compared to well-known semantic segmentation models, and a comprehensive assessment demonstrates that SARSNet consistently outperforms these models in all data subsets, including training, validation, and testing sets.

SARSNet—A Novel CNN Approach for SARWater Body Segmentation

This paper presents the SARSNet architecture, developed to address the growing challenges in Synthetic Aperture Radar (SAR) deep learning-based automatic water body extraction. Such a task is riddled with significant challenges, encompassing issues like cloud interference, scarcity of annotated dataset, and the intricacies associated with varied topography. Recent strides in Convolutional Neural Networks (CNNs) and multispectral segmentation techniques offer a promising avenue to address these predicaments. In our research, we propose a series of solutions to elevate the process of water body segmentation. Our proposed solutions span several domains, including image resolution enhancement, refined extraction techniques tailored for narrow water bodies, self-balancing of the class pixel level, and minority class-influenced loss function, all aimed at amplifying prediction precision and streamlining computational complexity inherent in deep neural networks. The framework of our approach includes the introduction of a multichannel Data-Fusion Register, the incorporation of a CNN-based Patch Adaptive Network augmentation method, and the integration of class pixel level balancing and the Tversky loss function. We evaluated the performance of the model using the Sentinel-1 SAR electromagnetic signal dataset from the Earth flood water body extraction competition organized by the artificial intelligence department of Microsoft. In our analysis, our suggested SARSNet was compared to well-known semantic segmentation models, and a comprehensive assessment demonstrates that SARSNet consistently outperforms these models in all data subsets, including training, validation, and testing sets.

A real-time SVM-based hardware accelerator for hyperspectral images classification in FPGA

Hyperspectral imaging can be conceptualized as a three-dimensional dataset of spectral information related to a particular landscape. Generally speaking, these are aerial photographs captured by Earth observation satellites. A useful analogy for a hyperspectral image is one of a cube formed with the image acquired along the X and Y axes and a third dimension of spectral bands of varying wavelengths. Given the wealth of data contained within these images, they have been employed in both civilian and military applications such as terrain recognition, urban development supervision, recognition of rare minerals, and various other objectives. The increased utilization of these images has garnered the interest of researchers striving to create solutions that may enable faster processing of the images via parallel processing. In this context, FPGA technology is an option capable of facilitating the implementation of such a system for observation satellites. This research is situated within this framework and aims to develop an FPGA-synthesized hardware accelerator to facilitate real-time hyperspectral image categorization. By taking this approach, hardware-specific solutions can be implemented for embedded applications that process hyperspectral images and can also be integrated with further image processing steps. The proposed accelerator was constructed based on an advanced algorithmic model, resulting in outcomes consistent with those generated by the software-based solution. The experimental results demonstrate that the engineered accelerator can attain a pixel classification time equal to or less than the pixel acquisition time, thus conforming to the real-time processing criteria concerning classification time. Further, the manufactured accelerator exhibits scalability that can classify distinct datasets with varying classes concurrently while maintaining a uniform logic resource utilization.

You Actually Look Twice At it (YALTAi): using an object detection approach instead of region segmentation within the Kraken engine

Layout Analysis (the identification of zones and their classification) is the first step along line segmentation in Optical Character Recognition and similar tasks. The ability of identifying main body of text from marginal text or running titles makes the difference between extracting the work full text of a digitized book and noisy outputs. We show that most segmenters focus on pixel classification and that polygonization of this output has not been used as a target for the latest competition on historical document (ICDAR 2017 and onwards), despite being the focus in the early 2010s. We propose to shift, for efficiency, the task from a pixel classification-based polygonization to an object detection using isothetic rectangles. We compare the output of Kraken and YOLOv5 in terms of segmentation and show that the later severely outperforms the first on small datasets (1110 samples and below). We release two datasets for training and evaluation on historical documents as well as a new package, YALTAi, which injects YOLOv5 in the segmentation pipeline of Kraken 4.1.

Variation Segmentation Layer in Deep Learning Network for SPECT Images Lesion Segmentation

Functional imaging, particularly SPECT Iodine-131 ablation imaging, has gained recognition as a useful clinical tool for diagnosing, treating, assessing as well as avoiding a variety of disorders, which includes metastasis. Nonetheless, SPECT imaging is conspicuously characterized by low resolution, high sensitivity, limited specificity, and a low signal-to-noise ratio. This is caused by the imaging data's visually similar characteristics of lesions amongst diseases. Concentrating on the automated diagnosis of diseases with SPECT Iodine-131 ablation imaging, in this work, three types of segmentation layers are used. This comprises a pixel classification layer, dice classification layer, and focal loss layer that will be tested to determine which segmentation layer is high for auto-segmentation lesions on SPECT Iodine-131 ablation imaging. The data preprocessing, which mostly entails data augmentation, is initially carried out to address the issue of small SPECT image sample sizes by using the geometric transformation operation. Deep Designer Network App was used to develop a 3D U-Net Convolutional Neural Network (CNN). The dice classification layer shows the highest accuracy for the thyroid uptake data set, which is 42.34, 0.7333, and 0.5789 for RMSD, DSC, and IoU, respectively. There is significance in using the dice classification layer in data sets that have various forms of ground truth labeling. On the other hand, the pixel classification layer is promising and workable for the multi-disease, multi-lesion classification task of SPECT Iodine-131 ablation imaging with a huge data set training.

Enhanced Fat Graft Viability and Remodeling Using a Helium-based Radiofrequency Device to Prepare the Recipient Site

BackgroundImprovements to autologous fat grafting for soft tissue augmentation are needed to overcome the unpredictable volume retention. Approaches such as fat harvesting and processing, injection technique, preparation of the recipient site, and supplemental biologics are topics of ongoing research. Here, an energy-based device was investigated as a stimulatory tool for recipient site preparation for improving fat graft retention.ObjectiveThe objective was to measure the stimulatory responses in fat grafts after 4 weeks when using a helium-based radiofrequency device to pretreat the recipient tissue.MethodsUsing an autologous fat grafting mouse model, the inguinal fat pad was grafted in a small cranial pocket after either a saline injection alone (control) or a saline injection followed by pretreatment (treated). The fat pad was resected after 4 weeks, sectioned and stained with immunofluorescence markers to investigate tissue remodeling.ResultsPretreatment resulted in higher viability of adipocytes, a higher concentration of viable ASCs in areas of adipose tissue regeneration, and localized macrophages in the areas of regeneration when compared to the control. There was no observable difference in vascularity or angiogenesis. The staining for ASCs was higher in the pretreated group in comparison with the control group (5.0% vs. 3.3%, p=0.36) when using a pixel classifier in QuPath in the viable adipose tissue regions.ConclusionsThe use of a helium-based radiofrequency device as a pretreatment tool appears to increase the viability of the adipose tissue likely due to higher concentration of ASCs. The apparent increase in viable ASCs may be due to enhanced proliferation or paracrine recruitment of these cells in response to the helium-based radiofrequency treatment.No Level AssignedThis journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.Bullet List of Important Points:Pretreatment of the fat graft recipient site increases the viability of the adipose tissue after 4 weeks in comparison with the control grafts.The increased viability is likely due to the observed increase in adipose-derived stem cells in the pretreated group.Pretreatment enhanced the adipose tissue remodeling as colocalization of adipose-derived stem cells and macrophages showed an active remodeling, whereas the control group exhibited more necrotic and fibrotic tissue.

Multi-scale multi-temporal landscape pattern analysis using high spatial urban scene classification

ABSTRACTUrban landscape pattern analysis is conducive to understanding the land cover distribution and changes and promoting urban spatial layout rationality and sustainable development. Previous research has often used low and medium spatial resolution data to classify urban land cover at the pixel level and then analyse landscape pattern indices. However, the underlying semantic information provided by pixel classification needs to consider the spatial correlation of surface features, and it is challenging to obtain high-level semantic information on urban functional areas and analyse the relationship between human activities and land use. Leveraging the abundant spatial characteristics in high-resolution remote sensing images, high spatial urban scene classification can infer the feature categories and spatial relationships between features to realize higher-level image interpretation and obtain regional urban scene details. And it contributes to urban planning and decision-making, better meeting the different demands. Therefore, this study aims at a novel methodological framework for landscape pattern analysis of cross-temporal multi-scale urban scene classification. The results imply that: (1) The high-resolution images can make up for the lack of information on urban functional zones in low and medium resolution images; (2) Landscape pattern analysis based on scene classification can analyse urban spatial relationships more rationally than traditional research; and (3) With the continuous advancement of urbanization, the urban functional zoning becomes more apparent, and the dominant industries have changed significantly from industry to commerce and services. This study expands the analytical application range of landscape ecology and remote sensing technology and provides new inspirations for urban planning, environmental monitoring, ecological protection.