High Spatial Resolution Images Research Articles

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

This study tested image fusion quality aiming at vegetation classification in the Kongquegou scenic location on the southern slope of Huaguo Mountain in Lianyungang, Jiangsu Province, China. Four fusion algorithms were used to fuse WorldView-2 multispectral and panchromatic images: GS (Gram-Schmidt) transform, Ehlers, Wavelet transform, and Modified IHS. The fusion effect was evaluated through visual comparison, quantitative index analysis, and vegetation classification accuracy. The study result revealed that GS and Wavelet transformation produced higher spectral fidelity and better-quality fusion images, followed by Modified IHS and Ehlers. In terms of vegetation classification, for the Wavelet transform, both spectral information and adding spatial structure provided higher accuracy and displayed suitability for vegetation classification in the selected area. Meanwhile, although the spectral features obtained better classification accuracy using the Modified IHS, adding spatial structure to the classification process produced less improvement and a lower robustness effect. The GS transform yielded better spectral fidelity but relatively low vegetation classification accuracy using spectral features only and combined spectral features and spatial structure. Lastly, the Ehlers method’s vegetation classification results were similar to those of the GS transform image fusion method. Additionally, the accuracy was significantly improved in the fused images compared to the multispectral image. Overall, Wavelet transforms showed the best vegetation classification results in the study area among the four fusion algorithms.

Use of RPA Images in the Mapping of the Chlorophyll Index of Coffee Plants

Coffee trading is an important source of income for the Brazilian commercial balance. Chlorophyll (Chl) are pigments responsible for converting radiation into energy; these pigments are closely related to the photosynthetic efficiency of plants, and the evaluation of the nutritional status of the coffee tree. The inversion method can be used for estimating the canopy chlorophyll content (Chlcanopy) using the leaf chlorophyll content (Chlleaf) and the leaf area index (LAI). The application of vegetation indices (VIs) in high spatial resolution images obtained from remotely piloted aircraft (RPA) can assist in the characterization of Chlcanopy in addition to providing vital and fast information for monitoring crops and aiding decision-making. This study aimed to identify which VIs adequately explain the Chl and evaluate the relationships between the VIs obtained from remotely piloted aircraft (RPA) images and the Chlleaf and Chlcanopy in coffee plants during the wet and dry seasons. The experiment was conducted on a Coffea arabica L. plantation in Lavras, Minas Gerais, Brazil. Images were collected on 26 November 2019 (wet), 11 August 2020 (dry), and 26 August 2021 (dry) by a multispectral camera embedded in a quadcopter. Plant height (H), crow diameter (D), and Chlleaf (a, b and total) data were collected in the field by a metre ruler (H and D) and sensor (Chlleaf). The LAI was calculated based on H and D. The Chlcanopy (a, b, and total) was calculated based on Chlleaf and LAI. The image processing was performed in Pix4D software, and postprocessing and calculation of the 21 VIs were performed in QGIS. Statistical analyses (descriptive, statistical tests, Pearson correlation, residuals calculation, and linear regression) were performed using the software R. The VIs from the RPA that best correlates to Chlcanopy in the wet season were the Modified Chlorophyll Absorption Ratio Index 2 (MCARI2RPA), Modified Simple Ratio (MSRRPA) and Simple Ratio (SRRPA). These VIs had high sensitivity and, therefore, were more affected by chlorophyll variability. For the two dry season studied days, there were no patterns in the relationships between Chlleaf, Chlcanopy, and the VIs. It was possible to use the Chl inversion method for the coffee during the wet season.

Use of In Vivo Imaging to Screen for Morphogenesis Phenotypes in Candida albicans Mutant Strains During Active Infection in a Mammalian Host.

Candida albicans is an important human pathogen. Its ability to switch between morphologic forms is central toits pathogenesis; these morphologic changes are regulated by a complex signaling network controlled in response to environmental stimuli. These regulatory components have been highly studied, but almost all studies use a variety of in vitro stimuli to trigger filamentation. To determine how morphogenesis is regulated during the pathogenesis process, we developed an in vivo microscopy system to obtain high spatial resolution images of organisms undergoing hyphal formation within the mammalian host. The protocol presented here describes the use of this system to screen small collections of C. albicans mutant strains, allowing us to identify key regulators of morphogenesis as it occurs at the site of infection. Representative results are presented, demonstrating that some regulators of morphogenesis, such as the transcriptional regulator Efg1, have consistent phenotypes in vitro and in vivo, whereas other regulators, such as adenyl cyclase (Cyr1), have significantly different phenotypes in vivo compared to in vitro.

Deep Feature Aggregation Framework Driven by Graph Convolutional Network for Scene Classification in Remote Sensing.

Scene classification of high spatial resolution (HSR) images can provide data support for many practical applications, such as land planning and utilization, and it has been a crucial research topic in the remote sensing (RS) community. Recently, deep learning methods driven by massive data show the impressive ability of feature learning in the field of HSR scene classification, especially convolutional neural networks (CNNs). Although traditional CNNs achieve good classification results, it is difficult for them to effectively capture potential context relationships. The graphs have powerful capacity to represent the relevance of data, and graph-based deep learning methods can spontaneously learn intrinsic attributes contained in RS images. Inspired by the abovementioned facts, we develop a deep feature aggregation framework driven by graph convolutional network (DFAGCN) for the HSR scene classification. First, the off-the-shelf CNN pretrained on ImageNet is employed to obtain multilayer features. Second, a graph convolutional network-based model is introduced to effectively reveal patch-to-patch correlations of convolutional feature maps, and more refined features can be harvested. Finally, a weighted concatenation method is adopted to integrate multiple features (i.e., multilayer convolutional features and fully connected features) by introducing three weighting coefficients, and then a linear classifier is employed to predict semantic classes of query images. Experimental results performed on the UCM, AID, RSSCN7, and NWPU-RESISC45 data sets demonstrate that the proposed DFAGCN framework obtains more competitive performance than some state-of-the-art methods of scene classification in terms of OAs.

High spatial resolution ToF-SIMS imaging and image analysis strategies to monitor and quantify early phase separation in amorphous solid dispersions

Amorphous solid dispersions (ASDs) are formulations with enhanced drug solubility and dissolution rate compared to their crystalline counterparts, however, they can be inherently thermodynamically unstable. This can lead to amorphous phase separation and drug re-crystallisation, phenomena that are typically faster and more dominant at the product’s surfaces. This study investigates the use of high-resolution time of flight-secondary ion mass spectrometry (ToF-SIMS) imaging as a surface analysis technique combined with image-analysis for the early detection, monitoring and quantification of surface amorphous phase separation in ASDs. Its capabilities are demonstrated for two pharmaceutically relevant ASD systems with distinct re-crystallisation behaviours, prepared using hot melt extrusion (HME) followed by pelletisation or grinding: (1) paracetamol-hydroxypropyl methylcellulose (PCM-HPMC) pellets with drug loadings of 10%–50% w/w and (2) indomethacin-polyvinylpyrrolidone (IND-PVP) ground material with drug loadings of 20%–85% w/w. PCM-HPMC pellets showed intense phase separation, reaching 100% PCM surface coverage within 1–5 months. In direct comparison, IND-PVP HME ground material was more stable with only a moderate formation of isolated IND-rich clusters. Image analysis allowed the reliable detection and quantification of local drug-rich clusters. An Avrami model was applied to determine and compare phase separation kinetics. The combination of chemical sensitivity and high spatial resolution afforded by SIMS was crucial to enable the study of early phase separation and re-crystallisation at the surface. Compared with traditional methods used to detect crystalline material, such as XRPD, we show that ToF-SIMS enabled detection of surface physical instability already at early stages of drug cluster formation in the first days of storage.

Fused LISS IV Image Classification using Deep Convolution Neural Networks

These days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively.

Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite

Atmospheric conditions vary significantly in terms of the temporal and spatial scales. Therefore, it is critical to obtain atmospheric parameters synchronized with an image for atmospheric correction based on radiative transfer calculation methods. On 3 July 2020, the high resolution and multimode imaging satellite, Gao Fen Duo Mo (GFDM), which was the first civilian high-resolution remote sensing satellite equipped with the Synchronization Monitoring Atmospheric Corrector (SMAC), was launched. The SMAC is a multispectral and polarization detection device that is used to retrieve atmospheric parameters that are time-synchronized with the image sensor of GFDM in the same field-of-view. On the basis of the atmospheric parameters obtained from the SMAC, a synchronization atmospheric correction (Syn-AC) method is proposed to remove the influence of the atmosphere and the adjacency effects to retrieve the surface reflectance. The Syn-AC method was applied in the experiments of synchronous atmospheric correction for GFDM images, where the surface reflectance retrieved via the Syn-AC method was compared with the field-measured values. In addition, the classical correction method, the FLAASH, was applied in the experiments to compare its performance with that of the Syn-AC method. The results indicated that the image possessed better clarity and contrast with the blurring effect removed, and the multispectral reflectance was in agreement with the field-measured spectral reflectance. The deviations between the reflectance retrievals of Syn-AC and the field-measured values of the selected targets were within 0.0625, representing a higher precision than that of the FLAASH method (the max deviation was 0.2063). For the three sites, the mean relative error of Syn-AC was 19.3%, and the mean relative error of FLAASH was 76.6%. Atmospheric correction based on synchronous atmospheric parameters can improve the quantitative accuracy of remote sensing images, and it is meaningful for remote sensing applications.

Estimating Rangeland Fine Fuel Biomass in Western Texas Using High-Resolution Aerial Imagery and Machine Learning

Rangeland fine fuel biomass is a key factor in determining fire spread and intensity, while the accuracy of biomass estimation is limited due to inherent heterogeneity in rangeland ecosystems. In this study, high spatial resolution (0.23 m) images were used to classify fuel types and predict rangeland fine fuel biomass in west Texas based on the random forest algorithm. Two biomass models, including one with the fuel type, original spectral bands, and vegetation indices as explanatory variables, and the other that contained a combination of the fuel type, original spectral bands, vegetation, and texture indices as explanatory variables, were assessed. Furthermore, the biomass models were also examined by upscaling the remote sensing images from high to medium (30 m) spatial resolution with the spectral curves derived from Landsat images. The fuel type map had an accuracy of more than 95%, and herbaceous fuel types were kept for estimating fine fuel biomass. The results showed that around 76% and 80% of biomass variances were explained by models without texture indices and with texture indices, respectively. The fuel type and the normalized difference vegetation index (NDVI) were two significant input variables influencing fine fuel biomass for both models and adding texture indices contributed to the improvement of model accuracy. An upscaling analysis for biomass estimation using medium spatial resolution imagery showed that approximately 60% of the variance in biomass was explained by the model. The addition of fractional vegetation cover improved the model performance by explaining an additional 5% of the variance in biomass estimation. These findings indicate that high spatial resolution images have the potential to effectively estimate rangeland fuel types and fine fuel biomass, which can be helpful for mapping the spatial distribution of fine fuels to aid in monitoring and fire management on rangelands.

Parcel-level mapping of apple orchard in smallholder agriculture areas based on feature-level fusion of VHR image and time-series images

ABSTRACT Accurate and reliable parcel-level apple orchard mapping is required for many precise agriculture application models, including planting suitability evaluation, standardized production, and personal agricultural operation loan approval. However, in hilly areas where smallholder management predominates, the highly fragmented and heterogeneous agricultural landscape means that fine parcel-level apple orchard mapping remains challenging. This paper proposes a parcel-level apple orchard mapping method based on feature-level spatiotemporal data fusion, which is suitable for hilly areas where smallholder management predominates. First, a hierarchical strategy that simulates human image cognition processing was used to extract redundant candidate parcels from a very high spatial resolution (VHR) image (Google Earth image with a spatial resolution of 0.6 m). Second, deep learning models, including a Depth-wise Asymmetric Bottleneck Network (DABNet) and long short-term memory (LSTM), were used to extract implicit spatial and time series features of the parcels. Third, the implicit features extracted by the deep learning models were formatted into meta-features, which then formed the feature space together with the morphological and geographical features of the parcel. Fourth, based on the constructed parcel feature space, a random forests (RF) model was used to classify candidate parcels. The experiment was carried out in the town of Guanli, southwest of Qixia city, Shandong Province, China: 21,123 apple orchard parcels were extracted from 31,235 candidate parcels. The overall accuracy (OA) of the parcel-level mapping result was 0.919. The parcel features were combined according to their types, and the performance of different feature combinations for parcel classification was further compared, demonstrating that the proposed meta-features had a stronger spatial information description capability than traditional features. Moreover, the mean decrease in the accuracy (MDA) index was used to evaluate the importance of each feature. And spatial-information-related meta-features were revealed to play the most important role in parcel classification. This method provides methodological references for parcel-level orchard mapping in hilly areas where smallholder management predominates and can be applied to improve the monitoring of orchards in such areas.

Data-Driven Random Forest Models for Detecting Volcanic Hot Spots in Sentinel-2 MSI Images

Volcanic thermal anomalies are monitored with an increased application of optical satellite sensors to improve the ability to identify renewed volcanic activity. Hotspot detection algorithms adopting a fixed threshold are widely used to detect thermal anomalies with a minimal occurrence of false alerts. However, when used on a global scale, these algorithms miss some subtle thermal anomalies that occur. Analyzing satellite data sources with machine learning (ML) algorithms has been shown to be efficient in extracting volcanic thermal features. Here, a data-driven algorithm is developed in Google Earth Engine (GEE) to map thermal anomalies associated with lava flows that erupted recently at different volcanoes around the world (e.g., Etna, Cumbre Vieja, Geldingadalir, Pacaya, and Stromboli). We used high spatial resolution images acquired by a Sentinel-2 MultiSpectral Instrument (MSI) and a random forest model, which avoids the setting of fixed a priori thresholds. The results indicate that the model achieves better performance than traditional approaches with good generalization capabilities and high sensitivity to less intense volcanic thermal anomalies. We found that this model is sufficiently robust to be successfully used with new eruptive scenes never seen before on a global scale.

Surface finishing effects on the corrosion behavior and electrochemical activity of 2098-T351 aluminum alloy investigated using scanning microelectrochemical techniques

The effects of surface finishing on the corrosion behavior and electrochemical activity of AA2098-T351 (Al–Cu–Li alloy) were investigated on the basis of the correlation between surface chemistry, microstructure and electrochemical activity. The alloy was evaluated in the as-received and polished conditions. The morphology of the two types of surfaces was investigated using confocal laser scanning microscopy (CLSM), optical microscopy and optical 3D profilometry. The surface chemistry was analyzed by X-ray photoelectron spectroscopy (XPS) and energy dispersive X-ray spectroscopy (EDX). Scanning microelectrochemical techniques (namely, localized electrochemical impedance spectroscopy (LEIS), the scanning vibrating electrode technique (SVET) and scanning electrochemical microscopy (SECM) in potentiometric mode) were used to examine the electrochemical activity of the surfaces. The results showed that on the as-received surface, the near surface deformed layer (NSDL), which is composed of Mg-rich bands, influenced the corrosion activity of the alloy. Higher electrochemical activity and greater susceptibility to severe localized corrosion were related to the polished surface condition compared to the as-received one. • Surface finishing effects on chemical activity of 2098-T351 Al–Cu–Li alloy studied using scanning microelectrochemical techniques. • The effect of the near-surface deformed layer (NSDL) on 2098-T351 Al–Cu–Li was investigated for different surface finishes. • A solid contact magnesium ion-selective microelectrode (Mg 2+ ISME) employed to monitor Mg 2+ distributions over the surfaces. • SVET, LEIS, and SECM in potentiometric operation produced high spatial resolution images of reactive sites on the surfaces. • The surface chemistry, microstructure and electrochemical activity of the alloy with and without NSDL were correlated.

Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

In recent times, coronary artery disease (CAD) has become one of the leading causes of morbidity and mortality across the globe. Diagnosing the presence and severity of CAD in individuals is essential for choosing the best course of treatment. Presently, computed tomography (CT) provides high spatial resolution images of the heart and coronary arteries in a short period. On the other hand, there are many challenges in analyzing cardiac CT scans for signs of CAD. Research studies apply machine learning (ML) for high accuracy and consistent performance to overcome the limitations. It allows excellent visualization of the coronary arteries with high spatial resolution. Convolutional neural networks (CNN) are widely applied in medical image processing to identify diseases. However, there is a demand for efficient feature extraction to enhance the performance of ML techniques. The feature extraction process is one of the factors in improving ML techniques’ efficiency. Thus, the study intends to develop a method to detect CAD from CT angiography images. It proposes a feature extraction method and a CNN model for detecting the CAD in minimum time with optimal accuracy. Two datasets are utilized to evaluate the performance of the proposed model. The present work is unique in applying a feature extraction model with CNN for CAD detection. The experimental analysis shows that the proposed method achieves 99.2% and 98.73% prediction accuracy, with F1 scores of 98.95 and 98.82 for benchmark datasets. In addition, the outcome suggests that the proposed CNN model achieves the area under the receiver operating characteristic and precision-recall curve of 0.92 and 0.96, 0.91 and 0.90 for datasets 1 and 2, respectively. The findings highlight that the performance of the proposed feature extraction and CNN model is superior to the existing models.

A dual key-and-lock probe for high-fidelity visualizing leather-induced neuroinflammation process via an ICT-TICT integrated ratiometric two-photon platform

The neuroinflammatory response is a cascade process by which the nervous system resists infection and injury. Excessive inflammatory responses, however, can induce neurodegenerative disease. Optical imaging operation make it possible for noninvasive molecular level detection and identification of the physiological and pathological processes due to its simple structure, high sensitivity and high spatial resolution imaging capability. Among the various probes, dual-key-and-lock systems are particularly interesting for their enhanced specificity and multiplex detection. Using viscosity and H2O2 as dual response sites, we successfully constructed a dual-key-and-lock probe with the AND logic gate (DLAK-H2O2), which manifested significant fluorescence signal changes only in the presence of both viscosity and H2O2. Inflammatory responses are accompanied by increased intracellular viscosity levels and H2O2 content, whereas the viscosity changes are not evident during other physiological or pathological processes such as oxidative stress, thus ultimately enabling the precise imaging of neuroinflammation.

New generation of Planet satellites generates images with eight spectral bands

The PlanetScope constellation continues to break paradigms in Remote Sensing . The System allows the daily monitoring of the entire Planet, from daily images of high spatial resolution (1 to 5 meters). Planet operates with a constellation of more than 200 nanosatellites. Together, these devices provide a dataset of Earth observation images unprecedented in history . It's a unique combination of coverage, frequency and resolution.

Recent Advances in Molecular Fluorescent Probes for CYP450 Sensing and Imaging

Cytochrome P450 (CYP450) is a major drug-metabolizing enzyme system mainly distributed in liver microsomes and involved in the metabolism of many endogenous substances (such as fatty acids and arachidonic acids), and exogenous compounds (such as drugs, toxicants, carcinogens, and procarcinogens). Due to the similarity in structures and catalytic functions between CYP450 isoforms, the lack of effective selective detection tools greatly limits the understanding and the research of their respective physiological roles in living organisms. Until now, several small-molecular fluorescent probes have been employed for selective detection and monitoring of CYP450s (Cytochrome P450 enzymes) in vitro or in vivo owing to the tailored properties, biodegradability, and high temporal and spatial resolution imaging in situ. In this review, we summarize the recent advances in fluorescent probes for CYP450s (including CYP1, CYP2, and CYP3 families), and we discuss and focus on their identification mechanisms, general probe design strategies, and bioimaging applications. We also highlight the potential challenges and prospects of designing new generations of fluorescent probes in CYP450 studies, which will further enhance the diversity, practicality, and clinical feasibility of research into CYP450.

Detecting pine wilt disease at the pixel level from high spatial and spectral resolution UAV-borne imagery in complex forest landscapes using deep one-class classification

Pine wilt disease (PWD) poses a serious threat to the worldwide pine forest resources. Unmanned aerial vehicle (UAV) remote sensing has been widely used for PWD control, due to its flexibility and efficiency. Although pixel-level detection can obtain fine detection boundaries, there have been few related works in complex scenes because of the difficulty of setting a preset category system and the poor generalization. A preset category system establishes which categories are to be labeled, and is necessary for traditional pixel-level detection. However, the poor generalization leads to an obvious accuracy drop when detecting PWD in new scenes. In the proposed approach, to address the first issue, one-class classification (OCC) is introduced to detect diseased pixels, focusing only on the category of diseased pine trees. However, the numerous objects but low PWD pixel proportion makes the model optimization unbalanced, for which balanced unbiased detection risk estimation is proposed. To address the second issue, a novel model consisting of three-dimensional (3D) convolutional layers and transformer blocks is proposed to extract more robust features. A novel PWD detection framework based on deep OCC is finally proposed to achieve fine pixel-level PWD detection results. PWD detection experiments were conducted on eight UAV H2 (high spatial and spectral resolution) image strips. In total, 300 PWD samples from strip 1 (accounting for roughly 0.009 % of the total pixels) and 400 unlabeled pixels formed the training set. The test experiments were conducted in the remaining seven strips to validate the model generalization. Satisfactory quantitative results (F1-score greater than 0.9) were obtained for all the test strips. The results indicate that the proposed method has a powerful ability to detect PWD in pine trees, even when the PWD proportion is low, and shows better model generalization than the traditional pixel-level detection methods.

Endoscope-integrated indocyanine green video angiography and the detection of the fragile periventricular collaterals associated with moyamoya disease: illustrative cases

Hemorrhagic moyamoya disease (MMD) and the fragile periventricular collaterals are known to have a causal relationship. Digital subtraction angiography and magnetic resonance angiography have shown the presence of fragile periventricular moyamoya vessels. However, dynamic fragile periventricular moyamoya vessels have never been observed under direct vision. The authors treated two patients with hemorrhagic MMD: a 42-year-old man with intraventricular hemorrhage and a 47-year-old woman with intracerebral hemorrhage. Endoscope-integrated indocyanine green video angiography (EICG angiography) could visualize the dynamic fragile periventricular collaterals. In particular, EICG angiography enabled visualization of invisible moyamoya vessels buried in the subependyma and characterization of the blood flow in the moyamoya vessels located inside the lateral ventricles and hematoma cavity. EICG angiography can confirm the fragile periventricular collaterals associated with MMD by direct visualization. The high spatial resolution and real-time imaging can help to avoid accidental hemorrhage in and after evacuation of hemorrhage in patients with MMD.

SNNFD, spiking neural segmentation network in frequency domain using high spatial resolution images for building extraction

• Propose SNNFD for building extraction from different images. • Outperform typical frameworks for building extraction from different images. • Propose first spiking neural network in frequency domain for building extraction. • Propose an objective evaluation strategy by mimicking practical applications. Up-to-date building maps are fundamental to urban development and analysis. However, detecting buildings from images with different spatial resolutions and ground object patterns from various imaging sensors is a challenge. Current models mostly have difficulties in extracting buildings with poor boundaries due to the various building appearances and sizes. Moreover, most published methods are trained and evaluated on subsets from the same dataset whose images are captured from one imaging sensor with similar ground object patterns, making it difficult to evaluate the transferability objectively. To address this issue, a spiking neural network in the frequency domain (SNNFD) is proposed to enhance the model transferability and the feature capability of buildings with different sizes by synthesizing frequency domain and spatial domain learning. Spiking convolution is adopted in the frequency learning module to enhance the model learning ability by mimicking the learning process of human brain. The learned frequency features are concatenated and transformed to the spatial domain, and used to generate building-extraction result images by convolution networks. SNNFD is evaluated on two datasets with different spatial resolutions (0.3–2.5 m) from different imaging sensors (Quickbird, Worldview, IKONOS, ZY-3) of different study areas (worldwide). It is compared with five recently proposed semantic segmentation frameworks (Unet, Segnet, DeepLabv3, BiSeNet, F 3 -Net), and obtains a minimum of 6.33 % higher accuracy with a strong transferability in detecting different sizes of building instances. Specifically, the proposed model improves the segmentation performance of small building instances by at least 6.5 % compared with the five segmentation frameworks through synthesis of spiking convolution in the frequency learning domain in the model construction. Moreover, details of building boundaries are better maintained by SNNFD, offering the possibility of detecting buildings for practical applications.

High spatial resolution imaging of the charge injection yield at hematite using scanning electrochemical cell microscopy

In-depth studies of charge injection and separation processes are important in characterizing the photoelectrochemical (PEC) properties of semiconductor materials, but high spatial resolution observations of the charge injection yield of such materials have not yet been reported. Here, scanning electrochemical cell microscopy (SECCM) is used for spatially resolved electrochemical imaging of the charge injection yield for a model material (hematite). Hydrogen peroxide is introduced as a hole scavenger to reduce the recombination of electrons and holes in the material, thereby achieving a division between charge injection and separation processes during PEC oxidation. Then, by calculating the ratio between the photocurrents before and after the addition of hydrogen peroxide, the distribution of the charge injection yield in the hematite film can be obtained at the nanoscale. The co-imaging of the morphology and the charge injection yield of hematite using SECCM makes it possible to observe the inverse dependence of the charge injection yield on the film thickness, which indicates that the main influence on the photocurrent is the charge injection process. The successful development of this nanoscale imaging method could provide more information to help elucidate the mechanism of the PEC process and, eventually, to aid in the design of more effective PEC materials.

A new spectral and spatial framework for detecting buildings with special roofing in hyperspectral images

One of the analyses in hyperspectral images is target detection. With recent developments and the creation of high spatial resolution images, the need to use both spectral and spatial information in the target detection has increased. Recently, an effective approach for spectral and spatial classification has been proposed using minimum spanning forest (MSF) algorithm. It was attempted to improve this approach for target detection in hyperspectral images. In the proposed method, the spectral image was primarily segmented using the watershed algorithm. Afterwards, for the objects resulting from segmentation, five spatial properties of area, environment, strength, meaning intensity, and entropy were extracted. Finally, the detection operation was performed utilizing the marker-based MSF algorithm. The above-mentioned techniques were applied to three hyperspectral images, first Toulouse, second Toulouse and Quebec. The results of quantitative and qualitative evaluations showed that the proposed method improved the kappa coefficient by 40, 34 and 23% in comparison with the spectral angle measurement (SAM) algorithm in the first Toulouse, second Toulouse and Quebec images, respectively.