Editorial: Hyperspectral imaging in environmental monitoring and analysis

Hyperspectral remote sensing stands as a vital tool in the realm of environmental monitoring and analysis, offering a comprehensive perspective by capturing subtle information in an extensive range of wavelengths. This capacity enables the creation of distinctive spectral signatures for every material within a given scene. The unique advantage lies in its ability to enhance the efficiency of environmental monitoring tasks, such as precise vegetation classification, target detection and landcover segmentation. By harnessing the richness of spectral information, hyperspectral imagery empowers researchers and decision-makers to delve deeper into the details of ecosystems, enabling more accurate and insightful analyses crucial for sustainable resource management and informed decision-making in the face of evolving environmental challenges

An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)

High-resolution urban image clustering has remained a challenging task. This is mainly because its performance strongly depends on the discrimination power of features. Recently, several studies focused on unsupervised learning methods by autoencoders to learn and extract more efficient features for clustering purposes. This paper proposes a Boosted Convolutional AutoEncoder (BCAE) method based on feature learning for efficient urban image clustering. The proposed method was applied to multi-sensor remote-sensing images through a multistep workflow. The optical data were first preprocessed by applying a Minimum Noise Fraction (MNF) transformation. Then, these MNF features, in addition to the normalized Digital Surface Model (nDSM) and vegetation indexes such as Normalized Difference Vegetation Index (NDVI) and Excess Green (ExG(2)), were used as the inputs of the BCAE model. Next, our proposed convolutional autoencoder was trained to automatically encode upgraded features and boost the hand-crafted features for producing more clustering-friendly ones. Then, we employed the Mini Batch K-Means algorithm to cluster deep features. Finally, the comparative feature sets were manually designed in three modes to prove the efficiency of the proposed method in extracting compelling features. Experiments on three datasets show the efficiency of BCAE for feature learning. According to the experimental results, by applying the proposed method, the ultimate features become more suitable for clustering, and spatial correlation among the pixels in the feature learning process is also considered

Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions

Application of 30-meter global digital elevation models for compensating rational polynomial coefficients biases

Generation of precise digital elevation models (DEMs) from stereo satellite images by using rational polynomial coefficients (RPCs) usually needs several ground control points (GCPs). This is mainly due to RPCs biases. However, since GCPs collection is a time consuming and expensive process, global DEMs (GDEMs), as the most inexpensive geospatial information, can be used to improve stereo satellite imagery-based DEMs (IB-DEMs). In this study, a 2.5 D mutual information based DEM matching, between a GDEM and an IB-DEM, was introduced for bias correction of satellite stereo images. Three well-known 30-meter GDEMs, namely, SRTM, ASTER, and AW3D30, were used and compared to assess the efficiency of this approach. The performance of the proposed method was evaluated by processing the stereo images acquired by CARTOSAT-1 satellite from two regions with flat, hilly, and mountainous topography. Evaluation results revealed that the proposed method could significantly improve the geometric accuracy of IB-DEM using all GDEMs

Sumac silver novel biodegradable nano composite for bio-medical application: antibacterial activity

The development of reliable and ecofriendly approaches for the production of nanomaterials is a significant aspect of nanotechnology nowadays. One of the most important methods, which shows enormous potential, is based on the green synthesis of nanoparticles using plant extract. In this paper, we aimed to develop a rapid, environmentally friendly process for the synthesis silver nanoparticles using aqueous extract of sumac. The bioactive compounds of sumac extract seem to play a role in the synthesis and capping of silver nanoparticles. Structural, morphological and optical properties of the nanoparticles were characterized using FTIR, XRD, FESEM and UV-Vis spectroscopy. The formation of Ag-NP was immediate within 10 min and confirmed with an absorbance band centered at 438 nm. The mean particle size for the green synthesized silver nanoparticles is 19.81 ± 3.67 nm and is fairly stable with a zeta potential value of −32.9 mV. The bio-formed Ag-NPs were effective against E. coli with a maximum inhibition zone of 14.3 ± 0.32 mm

Distorted Taste and Impaired Oral Health in Patients with Sicca Complaints

Senses of smell and taste, saliva flow, and dental status are considered as important factors for the maintenance of a good nutritional status. Salivary secretory rates, chemosensory function, burning mouth sensation, halitosis and dental status were investigated in 58 patients with primary Sjögren’s syndrome (pSS), 22 non-Sjögren’s syndrome sicca (non-SS) patients, and 57 age-matched healthy controls. A significantly greater proportion of patients with pSS and non-SS had ageusia, dysgeusia, burning mouth sensation, and halitosis compared to controls. Patients with pSS had significantly lower olfactory and gustatory scores, and significantly higher caries experience compared to controls. Patients with pSS and non-SS patients had significantly lower unstimulated and stimulated whole saliva secretory rates compared to controls. The findings indicated that several different aspects of oral health were compromised in both, patients with pSS and non-SS, and this may affect their food intake and, hence, their nutritional status. Although non-SS patients do not fulfill Sjögren’s syndrome classification criteria, they have similar or, in some cases, even worse oral complaints than the patients with pSS. Further studies are needed to investigate food preferences, dietary intake, and nutritional status in these two patient groups in relation to their health condition

Stability, optimum ultrasonication, and thermal and electrical conductivity estimation in low concentrations of Al12Mg17 nanofluid by dynamic light scattering and beam displacement method

Abstract The thermal conductivity and stability of nanofluids pose challenges for their use as coolants in thermal applications. The present study investigates the heat transfer coefficient (HTC) of an Al12Mg17 nanofluid through the utilization of a novel beam displacement method. The study also examines the nanofluid's stability, particle size distribution (PSD), TEM micrograph, and electrical conductivity. From three distinct categories of surfactants, a particular surfactant (CTAB) was chosen to disperse Al12Mg17 nanoparticles in DI water, and subsequently, a two-step method was employed to generate the nanofluid. Dispersion stability is visually monitored and quantified with a zeta potential test. HTC and PSD are measured using optical setups. To evaluate the results, the HTC obtained from the beam displacement method is compared with that of the KD2 Pro apparatus, and the PSD findings are analyzed through TEM micrographs. The results show that a 0.16 vol.% CTAB is the maximum stability for 0.025 vol.% Al12Mg17 nanofluid properly. The optimum ultrasonication period is 2 h, yielding a peak PSD of 154 nm. Increasing nanoparticle concentration enhances HTC up to 40% compared to the base fluid at 0.05 vol.%. Electrical conductivity increases linearly from 155 to 188 μ $${\rm S}/\mathrm{cm}$$ S / cm with nanoparticle concentration. Optical methods for measuring HTC in nanofluids offer the advantage of early results, prior to bulk motion. Thus, the application of nanofluids in thermal systems necessitates the development of optical techniques to improve accuracy