The development of remote sensing images in recent years has made it possible to identify materials in inaccessible environments and study natural materials on a large scale. But hyperspectral images ...(HSIs) are a rich source of information with their unique features in various applications. However, several problems reduce the accuracy of HSI classification; for example, the extracted features are not effective, noise, the correlation of bands, and most importantly, the limited labeled samples. To improve accuracy in the case of limited training samples, we propose a multiscale dual-branch residual spectral-spatial network with attention to the HSI classification model named MDBRSSN in this article. First, due to the correlation and redundancy between HSI bands, a principal component analysis operation is applied to preprocess the raw HSI data. Then, in MDBRSSN, a dual-branch structure is designed to extract the useful spectral-spatial features of HSI. The advanced feature, multiscale abstract information extracted by the convolution neural network, is applied to image processing, which can improve complex hyperspectral data classification accuracy. In addition, the attention mechanisms applied separately to each branch enable MDBRSSN to optimize and refine the extracted feature maps. Such an MDBRSSN framework can learn and fuse deeper hierarchical spectral-spatial features with fewer training samples. The purpose of designing the MDBRSSN model is to have high classification accuracy compared to state-of-the-art methods when the training samples are limited, which is proved by the results of the experiments in this article on four datasets. In Salinas, Pavia University, Indian Pines, and Houston 2013, the proposed model obtained 99.64%, 98.93%, 98.17%, and 96.57% overall accuracy using only 1%, 1%, 5%, and 5% of labeled data for training, respectively, which are much better compared to the state-of-the-art methods.
Recently, many efforts have been concentrated on land use land cover (LULC) classification due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and climate ...change. However, various aspects have attracted hyperspectral imaging due to there being informative discriminative features, such as spectral-spatial features. To this end, this paper is a comprehensive and systematic review of LULC classification using hyperspectral images by reviewing four significant research investigations. Moreover, the four investigations have addressed the following points: (1) the main components of the hyperspectral imaging, the modes of hyperspectral imaging with data acquisition methods, and the intrinsic differences between hyperspectral image and multispectral image, (2) the role of machine learning in LULC classification, and the standard deep learning methods: Convolution Neural Network (CNN), Stacked Autoencoder (SAE), Deep Belief Network (DBN), Recurrent Neural Network (RNN), and Generative Adversarial Network (GAN), (3) the standard benchmark hyperspectral datasets and the evaluation criteria, (4) the main challenges of LULC classification with the possible solutions for the limited training samples issue, the promising future directions, and finally the recent applications for LULC classification.
•A novel neural network is proposed to predict mechanical response of lattice-based metamaterial.•A simple method based on GCN for graph-level feature extraction was proposed.•The nonlinear ...stress–strain relationship of lattice metamaterials is precisely predicted.•The proposed network outperforms traditional Artificial Neural Networks.
Predicting the stress–strain curve of lattice-based metamaterials is crucial for their design and application. However, the complex nonlinear relationship between the mesoscopic structure of lattice materials and their macroscopic mechanical behavior makes prediction challenging. In this study, beam element models of over 20,000 lattice structures were established using Python scripts, and calculations were performed by ABAQUS to obtain training and testing datasets. The spatial features of each lattice-based metamaterial were then encoded into a graph, a data structure recognizable by machine learning algorithm. Utilizing machine learning methods, a Structure to Sequence Neural Network was constructed and trained, achieving rapid prediction of the compressive stress–strain curves for lattice-based metamaterials. Afterwards, several lattice structures were randomly selected and 3D printed. The accuracy of the simulation results as well as machine learning predictions was validated through quasi-static compression experiments. It is revealed that the proposed Neural Network model outperforms the traditional Artificial Neural Networks as the errors are reduced while the Coefficient of Determination is higher. The results demonstrate the accurate fitting between the complex spatial features of the lattice-based metamaterials and their stress–strain curves, which provides a potential methodology for inverse optimization of the lattice-based metamaterials in the future.
Hyperspectral images (HSIs) not only possess abundant spectral features but also present a detailed spatial distribution of land cover, and they have significant advantages in the fine classification ...of ground materials. Recently, using convolutional neural networks (CNNs) to extract spectral-spatial features has become an effective way for HSI classification. However, conventional convolution kernels learn features from fixed regular square regions, and rich spatial information has not been effectively explored. In this letter, an end-to-end model named spectral-spatial residual graph attention network (S 2 RGANet) is developed for HSI classification, and it has two crucial elements, including spectral residual and graph attention convolution modules. At first, two spectral residual modules are employed to capture discriminant spectral features. Then, graphs are constructed to reveal the relationship between points in local neighborhoods. By graph attention mechanism, local spatial information is adaptively aggregated from neighboring nodes. Experiments on two public HSI datasets demonstrate that the S 2 RGANet is significantly superior to some state-of-the-art (SOTA) methods with limited training samples.
In this paper, we propose a video based spatial-temporal convolutional neural network for fire smoke recognition. The model concatenates the appearance features and the motion features followed by a ...convolution layer to implement spatial-temporal feature fusion. To reduce the influence of background of no-smoke, we use an attention module to capture salience features from the input image. Experiments on the self-created dataset show that the presented method is valid, which achieves a detection rate of 97.5% and accuracy rate of 96.8%.
Pollution monitoring system is used to monitor the air pollution throughout the city, which cause pollution over a specified limit. The sensor nodes are attached to the lamp post. The sensors are ...organized into clusters and form a mesh network of nodes that provide both single hop and multihop connectivity with the base station. The GPS enabled sensor nodes finds location in order to detect the pollution occurring place. A hybrid model is proposed in this work which combines the spatial and temporal features for prediction. This model use the real time air quality information in a city by measuring the pollution information using sensors and data sets.
Just over a decade has passed since the concept of morphological profile was defined for the analysis of remote sensing images. Since then, the morphological profile has largely proved to be a ...powerful tool able to model spatial information (e.g., contextual relations) of the image. However, due to the shortcomings of using the morphological profiles, many variants, extensions, and refinements of its definition have appeared stating that the morphological profile is still under continuous development. In this case, recently introduced theoretically sound attribute profiles (APs) can be considered as a generalization of the morphological profile, which is a powerful tool to model spatial information existing in the scene. Although the concept of the AP has been introduced in remote sensing only recently, an extensive literature on its use in different applications and on different types of data has appeared. To that end, the great amount of contributions in the literature that address the application of the AP to many tasks (e.g., classification, object detection, segmentation, change detection, etc.) and to different types of images (e.g., panchromatic, multispectral, and hyperspectral) proves how the AP is an effective and modern tool. The main objective of this survey paper is to recall the concept of the APs along with all its modifications and generalizations with special emphasis on remote sensing image classification and summarize the important aspects of its efficient utilization while also listing potential future works.
Studies on the classification of hyperspectral images (HSIs) based on deep learning are in full swing, especially the spectral-spatial dependent global learning (SSDGL) framework, which is both ...efficient and robust. However, the global convolutional long short-term memory (GCL) module under this framework fails to take full consideration of the spectral characteristics contained in HSIs, and the hierarchically balanced (H-B) sampling strategy introduced in this framework prevents the training process from converging smoothly. In this article, we develop a novel regularized spectral-spatial global learning (RSSGL) framework. Compared with SSDGL, the proposed framework mainly makes three improvements. Above all, aiming at the problem that the GCL module used in SSDGL cannot fully tap the local spectral dependence, we apply 3-D convolution to the gated units of long short-term memory (LSTM) as an alternative to the GCL module for adjacent and nonadjacent spectral dependencies learning. Furthermore, to extract the most discriminative features, an improved statistical loss regularization term is developed, in which we introduce a simple but effective diversity-promoting condition to make it more reasonable and suitable for deep metric learning in HSI classification. Finally, to effectively address the performance oscillation caused by the H-B sampling strategy, the proposed framework adopts an early stopping strategy to save and restore the optimal model parameters, making it more flexible and stable. Experiments conducted on three representative datasets show that the proposed RSSGL has superior classification performance compared with the existing relatively excellent research methods. The source code is released at https://github.com/swiftest/RSSGL .
Spectral-spatial classification of hyperspectral images (HSIs) has been extensively studied. Although the importance of spatial information for classification of HSIs is widely proven in the ...literature, the definition of effective techniques for the extraction of spatial information is still a challenging and open research issue. In this letter, a semantic edge-aware structure preserving image filtering technique is presented to accurately model spatial information in HSI classification. The experimental results on the three real HSI data sets show the superiority of our model, which provides at least 2% higher classification accuracy than the best among the numerous literature models considered.
Abstract
Retrieving historical fine particulate matter (PM2.5) data is key for evaluating the long-term impacts of PM2.5 on the environment, human health and climate change. Satellite-based aerosol ...optical depth has been used to estimate PM2.5, but estimations have largely been undermined by massive missing values, low sampling frequency and weak predictive capability. Here, using a novel feature engineering approach to incorporate spatial effects from meteorological data, we developed a robust LightGBM model that predicts PM2.5 at an unprecedented predictive capacity on hourly (R2 = 0.75), daily (R2 = 0.84), monthly (R2 = 0.88) and annual (R2 = 0.87) timescales. By taking advantage of spatial features, our model can also construct hourly gridded networks of PM2.5. This capability would be further enhanced if meteorological observations from regional stations were incorporated. Our results show that this model has great potential in reconstructing historical PM2.5 datasets and real-time gridded networks at high spatial-temporal resolutions. The resulting datasets can be assimilated into models to produce long-term re-analysis that incorporates interactions between aerosols and physical processes.
A high-performance machine-learning model incorporating spatial effects was developed to estimate historical PM2.5 concentrations based on meteorological data. Capable of hourly resolution, this dataset will be of great value for understanding PM2.5's long-term climate and environmental effects and producing chemical-weather coupled reanalysis.
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