Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.

DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images

Multi-source data remote sensing provides innovative technical support for tree species recognition. Tree species recognition is relatively poor despite noteworthy advancements in image fusion methods because the features from multi-source data for each pixel in the same region cannot be deeply exploited. In the present paper, a novel deep learning approach for hyperspectral imagery is proposed to improve accuracy for the classification of tree species. The proposed method, named the double branch multi-source fusion (DBMF) method, could more deeply determine the relationship between multi-source data and provide more effective information. The DBMF method does this by fusing spectral features extracted from a hyperspectral image (HSI) captured by the HJ-1A satellite and spatial features extracted from a multispectral image (MSI) captured by the Sentinel-2 satellite. The network has two branches in the spatial branch to avoid the risk of information loss, of which, sandglass blocks are embedded into a convolutional neural network (CNN) to extract the corresponding spatial neighborhood features from the MSI. Simultaneously, to make the useful spectral feature transfer more effective in the spectral branch, we employed bidirectional long short-term memory (Bi-LSTM) with a triple attention mechanism to extract the spectral features of each pixel in the HSI with low resolution. The feature information is fused to classify the tree species after the addition of a fusion activation function, which could allow the network to obtain more interactive information. Finally, the fusion strategy allows for the prediction of the full classification map of three study areas. Experimental results on a multi-source dataset show that DBMF has a significant advantage over other state-of-the-art frameworks.

Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.

SFE2D: A Hybrid Tool for Spatial and Spectral Feature Extraction

A crucial task for integrated geoscientific image (geo-image) interpretation is the relevant geological representation of multiple geo-images, which demands high-dimensional techniques for extracting latent geological features from high-dimensional geo-images. A standalone mathematical tool called SFE2D (spatiospectral feature extraction in two-dimension) is developed based on independent component analysis (ICA), continuous wavelet transform (CWT), k-means clustering segmentation, and RGB color processing that iteratively separates, extracts, clusters, and visualizes the highly correlated and overlapped geological features from multiple sources of geo-images. The SFE2D offers spatial feature extraction and wavelet-based spectral feature extraction for further extraction of frequency-dependent features. We show that the SFE2D is a robust tool for automated pattern recognition, fast pseudo-geological mapping, and detection of regions of interest with a wide range of applications in different scales, from regional geophysical surveys to the interpretation of microscopic images.

A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching

Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications. Feature descriptors for grayscale image matching are well studied, but few descriptors are elaborately designed for HSI matching. HSI descriptors, which should have made good use of the spectral feature, are essential in HSI matching tasks. Therefore, this paper presents a descriptor for HSI matching, called HOSG-SIFT, which ensembles spectral features with spatial features of objects. First, we obtain the grayscale image by dimensional reduction from HSI and apply it to extract keypoints and descriptors of spatial features. Second, the descriptors of spectral features are designed based on the histogram of the spectral gradient (HOSG), which effectively preserves the physical significance of the spectral profile. Third, we concatenate the spatial descriptors and spectral descriptors with the same weights into a new descriptor and apply it for HSI matching. Experimental results demonstrate that the proposed HOSG-SIFT performs superior against traditional feature descriptors.

Amoeba: Automated Molecular Excitation Bayesian Line-fitting Algorithm

The hyperfine transitions of the ground-rotational state of the hydroxyl radical (OH) have emerged as a versatile tracer of the diffuse molecular interstellar medium. We present a novel automated Gaussian decomposition algorithm designed specifically for the analysis of the paired on-source and off-source optical depth and emission spectra of these OH transitions. In contrast to existing automated Gaussian decomposition algorithms, Amoeba (Automated Molecular Excitation Bayesian line-fitting Algorithm) employs a Bayesian approach to model selection, fitting all four optical-depth and four emission spectra simultaneously. Amoeba assumes that a given spectral feature can be described by a single centroid velocity and full width at half maximum, with peak values in the individual optical-depth and emission spectra then described uniquely by the column density in each of the four levels of the ground-rotational state, thus naturally including the real physical constraints on these parameters. Additionally, the Bayesian approach includes informed priors on individual parameters that the user can modify to suit different data sets. Here we describe Amoeba and establish its validity and reliability in identifying and fitting synthetic spectra with known (but hidden) parameters, finding that the code performs very well in a series of practical tests. Amoeba’s core algorithm could be adapted to the analysis of other species with multiple transitions interconnecting shared levels (e.g., the 700 MHz lines of the first excited rotational state of CH). Users are encouraged to adapt and modify Amoeba to suit their own use cases.

Prevalence of Extra Power-Law Spectral Components in Short Gamma-Ray Bursts

A prompt extra power-law (PL) spectral component that usually dominates the spectral energy distribution below tens of keV or above ∼10 MeV has been discovered in some bright gamma-ray bursts (GRBs). However, its origin is still unclear. In this paper, we present a systematic analysis of 13 Fermi short GRBs, as of 2020 August, with contemporaneous keV–MeV and GeV detections during the prompt emission phase. We find that the extra PL component is a ubiquitous spectral feature for short GRBs, showing up in all 13 analyzed GRBs. The PL indices are mostly harder than −2.0, which may be well reproduced by considering the electromagnetic cascade induced by ultrarelativistic protons or electrons accelerated in the prompt emission phase. The average flux of these extra PL components positively correlates with that of the main spectral components, which implies they may share the same physical origin.

I3D: An Improved Three-Dimensional CNN Model on Hyperspectral Remote Sensing Image Classification

Hyperspectral image data are widely used in real life because it contains rich spectral and spatial information. Hyperspectral image classification is to distinguish different functions based on different features. The computer performs quantitative analysis through the captured image and classifies each pixel in the image. However, the traditional deep learning-based hyperspectral image classification technology, due to insufficient spatial-spectral feature extraction, too many network layers, and complex calculations, leads to large parameters and optimizes hyperspectral images. For this reason, I proposed the I3D-CNN model. The number of classification parameters is large, and the network is complex. This method uses hyperspectral image cubes to directly extract spectral-spatial coupling features, adds depth separable convolution to 3D convolution to reextract spatial features, and extracts the parameter amount and calculation time at the same time. In addition, the model removes the pooling layer to achieve fewer parameters, smaller model scale, and easier training effects. The performance of the I3D-CNN model on the test datasets is better than other deep learning-based methods after comparison. The results show that the model still exhibits strong classification performance, reduces a large number of learning parameters, and reduces complexity. The accuracy rate, average classification accuracy rate, and kappa coefficient are all stable above 95%.