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Automatic classification of stellar spectra contributes to the study of the structure and evolution of the Milky Way and star formation. Currently available methods exhibit unsatisfactory spectral classification accuracy. This study investigates a method called DSRL, which is primarily used for automated and accurate classification of LAMOST stellar spectra based on MK classification criteria. The method utilizes discrete wavelet transform to decompose the spectra into high-frequency and low-frequency information, and combines residual networks and long short-term memory networks to extract both high-frequency and low-frequency features. By introducing self-distillation (DSRL-1, DSRL-2, and DSRL-3), the classification accuracy is improved. DSRL-3 demonstrates superior performance across multiple metrics compared to existing methods. In both three-class(F ,G ,K) and ten-class(A0, A5, F0, F5, G0, G5, K0, K5, M0, M5) experiments, DSRL-3 achieves impressive accuracy, precision, recall, and F1-Score results. Specifically, the accuracy performance reaches 94.50% and 97.25%, precision performance reaches 94.52% and 97.29%, recall performance reaches 94.52% and 97.22%, and F1-Score performance reaches 94.52% and 97.23%. The results indicate the significant practical value of DSRL in the classification of LAMOST stellar spectra. To validate the model, we visualize it using randomly selected stellar spectral data. The results demonstrate its excellent application potential in stellar spectral classification.

In the blazar catalog BZCAT the objects are divided into 4 types: BZB, BZQ, BZG, and BZU. In this paper BZG objects are studied in order to determine their physical nature. Of the 274 BZG objects, 150 have optical spectra in the SDSS spectroscopic catalog, for which a detailed spectral classification has been carried out. A radio study showed that these objects mostly (69%) have a flat radio spectrum with a spectral index less than ±0.5. Quasars predominate among these kinds of radio spectra. However, with increasing distance, on the average the radio spectrum becomes steeper.

Since the technology of remote sensing has been improved recently, the spatial resolution of satellite images is getting finer. This enables us to precisely analyze the small complex objects in a scene through remote sensing images. Thus, the need to develop new, efficient algorithms like spatial-spectral classification methods is growing. One of the most successful approaches is based on extinction profile (EP), which can extract contextual information from remote sensing data. Moreover, deep learning classifiers have drawn attention in the remote sensing community in the past few years. Recent progress has shown the effectiveness of deep learning at solving different problems, particularly segmentation tasks. This paper proposes a novel approach based on a new concept, which is differential extinction profile (DEP). DEP makes it possible to have an input feature vector with both spectral and spatial information. The input vector is then fed into a proposed straightforward deep-learning-based classifier to produce a thematic map. The approach is carried out on two different urban datasets from Pleiades and World-View 2 satellites. In order to prove the capabilities of the suggested approach, we compare the final results to the results of other classification strategies with different input vectors and various types of common classifiers, such as support vector machine (SVM) and random forests (RF). It can be concluded that the proposed approach is significantly improved in terms of three kinds of criteria, which are overall accuracy, Kappa coefficient, and total disagreement.

According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.

Galaxy clusters magnify background objects through strong gravitational lensing. Typical magnifications for lensed galaxies are factors of a few but can also be as high as tens or hundreds, stretching galaxies into giant arcs 1 , 2 . Individual stars can attain even higher magnifications given fortuitous alignment with the lensing cluster. Recently, several individual stars at redshifts between approximately 1 and 1.5 have been discovered, magnified by factors of thousands, temporarily boosted by microlensing 3 – 6 . Here we report observations of a more distant and persistent magnified star at a redshift of 6.2 ± 0.1, 900 million years after the Big Bang. This star is magnified by a factor of thousands by the foreground galaxy cluster lens WHL0137–08 (redshift 0.566), as estimated by four independent lens models. Unlike previous lensed stars, the magnification and observed brightness (AB magnitude, 27.2) have remained roughly constant over 3.5 years of imaging and follow-up. The delensed absolute UV magnitude, −10 ± 2, is consistent with a star of mass greater than 50 times the mass of the Sun. Confirmation and spectral classification are forthcoming from approved observations with the James Webb Space Telescope. A massive star at a redshift of 6.2, corresponding to 900 million years after the Big Bang, is magnified greatly by lensing of the foreground galaxy cluster WH0137–08.

Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

Owing to the limitation of spatial resolution and spectral resolution, deep learning methods are rarely used for the classification of multispectral remote sensing images based on the real spectral dataset from multispectral remote sensing images. This study explores the application of a deep learning model to the spectral classification of multispectral remote sensing images. To address the problem of the large workload with respect to selecting training samples during classification by deep learning, first, linear spectral mixture analysis and the spectral index method were applied to extract the pixels of impervious surfaces, soil, vegetation, and water. Second, through the Euclidean distance threshold method, a spectral dataset of multispectral image pixels was established. Third, a deep learning classification model, ResNet-18, was constructed to classify Landsat 8 OLI images based on pixels’ real spectral information. According to the accuracy assessment, the results show that the overall accuracy of the classification results can reach 0.9436, and the kappa coefficient can reach 0.8808. This study proposes a method that allows for the more optimized establishment of the actual spectral dataset of ground objects, addresses the limitations of difficult sample selection in deep learning classification and of spectral similarity in traditional classification methods, and applies the deep learning method to the classification of multispectral remote sensing images based on a real spectral dataset.

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Capsule networks can be considered to be the next era of deep learning and have recently shown their advantages in supervised classification. Instead of using scalar values to represent features, the capsule networks use vectors to represent features, which enriches the feature presentation capability. This paper introduces a deep capsule network for hyperspectral image (HSI) classification to improve the performance of the conventional convolutional neural networks (CNNs). Furthermore, a modification of the capsule network named Conv-Capsule is proposed. Instead of using full connections, local connections and shared transform matrices, which are the core ideas of CNNs, are used in the Conv-Capsule network architecture. In Conv-Capsule, the number of trainable parameters is reduced compared to the original capsule, which potentially mitigates the overfitting issue when the number of available training samples is limited. Specifically, we propose two schemes: (1) A 1D deep capsule network is designed for spectral classification, as a combination of principal component analysis, CNN, and the Conv-Capsule network, and (2) a 3D deep capsule network is designed for spectral-spatial classification, as a combination of extended multi-attribute profiles, CNN, and the Conv-Capsule network. The proposed classifiers are tested on three widely-used hyperspectral data sets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, including kernel support vector machines, CNNs, and recurrent neural network.

Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM named Minimum within-class and Maximum between-class scatter Support Vector Machine (MMSVM) is constructed to deal with the above problem. MMSVM merges the advantages of Fisher’s Discriminant Analysis (FDA) and SVM, and the comparative experiments on the Sloan Digital Sky Survey (SDSS) show that MMSVM performs better than SVM.