STTG-TTE: spatial–temporal gated multi-modality approach for travel time estimation based on temporal convolutional networks

Neural Computing and Applications - Travel time forecasting has become a core component of smart transportation systems, which assists both travelers and traffic organizers with route planning,...     

Landsat-satellite-based analysis of spatial–temporal dynamics and drivers of CyanoHABs in the plateau Lake Dianchi

ABSTRACT

Dianchi Lake, located in southwest China’s Yungui plateau, is facing severe eutrophication and frequent outbreaks of harmful cyanobacteria blooms (CyanoHABs). It is of great significance to monitor the occurrence and development of CyanoHABs in Dianchi Lake over a long period and analyse the main influences. Based on Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager 1986–2016 data, we derived the distribution of the CyanoHABs in Dianchi Lake, and analysed spatial–temporal dynamics of the CyanoHABs by correlation with nutrition, meteorological, and humanities data. The results showed that the first outbreak of CyanoHABs in Dianchi Lake occurred in 1987, which is likely to be influenced by a rapid increase of nutrients in the lake, while the weather conditions also have some impact on the CyanoHABs occurrence. After 1990, the frequency of CyanoHABs is relatively high in the water near Longmen village, Fubao Bay, Hui Bay, and the lake inlet of the Panlong River to the north of Waihai in Dianchi Lake from June to November every year. Moreover, the CyanoHABs increased year by year until 2000. This is closely related to population growth and economic development. Furthermore, a large amount of precipitation and small wind speeds can also promote the occurrence of CyanoHABs. After 2000, the frequency of CyanoHABs decreased, as the large-scale management of water pollution in Dianchi Lake achieved certain effects. The area and frequency of CyanoHABs from 2011 to 2014 are the smallest in the last 20 years, which may be related to the large-scale planting of Eichhornia crassipes in the north of Dianchi Lake.     

Orchard identification using landform and landscape factors based on a spatial–temporal classification framework

Ecological restoration measures have been undertaken in loess hilly and gully regions since the 1970s to prevent soil loss and to improve the ecological environment in those regions. Orchard construction was the main ecological measure undertaken in the Luo-Yu-Gou watershed, and in this article we propose a coupled maximum a posteriori decision rule and Markov random field (MAP-MRF) framework for orchard identification based on landform and landscape factors. Support vector machine (SVM) classification was first performed to obtain initial classification results for the years 2003 and 2008. A series of factors including landform factor, landscape factor, and the spatial–temporal neighbourhood factor are used to obtain land-cover change information including the change in orchard class. Finally, field experiments were carried out in the case study region of the Luo-Yu-Gou watershed, and based on the experimental results, it was found that the quantity error and the allocation error of the classification results for 2008 were 0.0441 and 0.1037, respectively.     

Analysis of inertial stick–slip motion error based on overturning moment

Microsystem Technologies - The stick–slip driving is widely used in the field of nanotechnology because of its high resolution and theoretically unlimited displacement. However, it suffers...     

A spatial–temporal analysis of impacts from human development on the Shih-men Reservoir watershed,Taiwan

Human activity such as the development of slope land around watersheds has dramatically affected the ecological environment in Taiwan. This situation has been aggravated by heavy precipitation from typhoons in the summertime. The results include serious soil erosion and mass movement in the Shih-men Reservoir watershed. In order to identify the most fragile areas and seek the triggering factors of landslide changes that can cause turbid currents in the Shih-men Reservoir watershed, this study integrates different types of satellite imagery and geographic information system data to determine changes in land cover and vegetation cover since the early 1970s. Results from spatial regression models indicate road and land uses are the main factors that lead to slope failure along roads and contribute to a large number of landslides in environmental hotspots like the Baishih River sub-watershed. Soil erosion estimates indicate a positive relationship between the increases in landslide and soil loss areas and the road system development. Therefore, human development has a significant negative influence both on sensitive mountainous watersheds and on critical environmental hotspots.     

Spectral–spatial co-clustering of hyperspectral image data based on bipartite graph

The high dimensionality of hyperspectral images are usually coupled with limited data available, which degenerates the performances of clustering techniques based only on pixel spectral. To improve the performances of clustering, incorporation of spectral and spatial is needed. As an attempt in this direction, in this paper, we propose an unsupervised co-clustering framework to address both the pixel spectral and spatial constraints, in which the relationship among pixels is formulated using an undirected bipartite graph. The optimal partitions are obtained by spectral clustering on the bipartite graph. Experiments on four hyperspectral data sets are performed to evaluate the effectiveness of the proposed framework. Results also show our method achieves similar or better performance when compared to the other clustering methods.     

Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks

Due to the low cost and capabilities of sensors, wireless sensor networks (WSNs) are promising for military and civilian surveillance of people and vehicles. One important aspect of surveillance is target localization. A location can be estimated by collecting and analyzing sensing data on signal strength, time of arrival, time difference of arrival, or angle of arrival. However, this data is subject to measurement noise and is sensitive to environmental conditions, so its location estimates can be inaccurate. In this paper, we add a novel process to further improve the localization accuracy after the initial location estimates are obtained from some existing algorithm. Our idea is to exploit the consistency of the spatial–temporal relationships of the targets we track. Spatial relationships are the relative target locations in a group and temporal relationships are the locations of a target at different times. We first develop algorithms that improve location estimates using spatial and temporal relationships of targets separately, and then together. We prove mathematically that our methods improve the localization accuracy. Furthermore, we relax the condition that targets should strictly keep their relative positions in the group and also show that perfect time synchronization is not required. Simulations were also conducted to test the algorithms. They used initial target location estimates from existing signal-strength and time-of-arrival algorithms and implemented our own algorithms. The results confirmed improved localization accuracy, especially in the combined algorithms. Since our algorithms use the features of targets and not the underlying WSNs, they can be built on any localization algorithm whose results are not satisfactory.     

Spatial–temporal changes in vegetation coverage in the global coastal zone based on GIMMS NDVI3g data

ABSTRACT

In this paper, we used the Global Inventory Modelling and Mapping Studies (GIMMS) third-generation Normalized Difference Vegetation Index (NDVI) (GIMMS NDVI3g) dataset. Based on GIMMS NDVI3g data over the global coastal zone from 1982 to 2014, the spatial–temporal characteristics of vegetation coverage were analysed by plotting the spatial pattern and monthly calendar of NDVI; furthermore, historical trends and future evolutions of vegetation coverage change at the pixel scale were studied by performing the Mann-Kendall trend test and calculating the trend slope (β) and Hurst index (H) of NDVI. The main findings are as follows: 1) Vegetation density exhibits dramatic differences in the global coastal zone. Specifically, desert belts mostly have perennial non-vegetation or low vegetation coverage, and tundra belts principally have moderate or high vegetation coverage; additionally, forest belts mainly have dense vegetation coverage. 2) In the global coastal zone, intra-annual variations in vegetation coverage show a ‘∩’-shaped curve with an obvious peak from June to September (maximum in July or August), while inter-annual variations show a fluctuating but generally slowly increasing trend over the entire study period; accordingly, variations in different subregions show significant differences. 3) At monthly, seasonal and annual scales, the overall vegetation coverage increases in the global coastal zone, while there are relatively few areas with decreasing vegetation coverage; furthermore, change trends of vegetation coverage in most areas will demonstrate relatively strong positive persistence in the future. 4) The increasing trend in high-latitude coastal tundra is extremely significant in the growing season because vegetation in the tundra belts is highly sensitive to climate change. 5) Areas with a decreasing trend of vegetation coverage exhibit spatial patterns of aggregation in the ‘circum urban agglomeration’ and ‘nearby desert belt’ regions, that is, the decreasing trend of vegetation coverage is relatively high in coastal urban agglomeration areas and desert belt peripheries. This paper is expected to provide knowledge to support vegetation conservation, ecosystem management, integrated coastal zone management and climate change adaptation in coastal areas.     

Hyperspectral anomaly detection using spectral–spatial features based on the human visual system

ABSTRACT

Anomaly detection (AD) is one of the most attracting topics within the recent 10 years in hyperspectral imagery (HSI). The goal of the AD is to label the pixels with significant spectral or spatial differences to their neighbours, as targets. In this paper, we propose a method that uses both spectral and spatial information of HSI based on human visual system (HVS). By inspiring the retina and the visual cortex functionality, the multiscale multiresolution analysis is applied to some principal components of hyperspectral data, to extract features from different spatial levels of the image. Then the global and local relations between features are considered based on inspiring the visual attention mechanism and inferotemporal (IT) part of the visual cortex. The effects of the attention mechanism are implemented using the logarithmic function which well highlights, small variations in pixels’ grey levels in global features. Also, the maximum operation is used over the local features for imitating the function of IT. Finally, the information theory concept is used for generating the final detection map by weighting the global and local detection maps to obtain the final anomaly map. The result of the proposed method is compared with some state-of-the-art methods such as SSRAD, FLD, PCA, RX, KPCA, and AED for two well-known real hyperspectral data which are San Diego airport and Pavia city, and a synthetic hyperspectral data. The results demonstrate that the proposed method effectively improves the AD capabilities, such as enhancement of the detection rate, reducing the false alarm rate and the computation complexity.     

Animated visualization of spatial–temporal trajectory data for air-traffic analysis

With increasing numbers of flights worldwide and a continuing rise in airport traffic, air-traffic management is faced with a number of challenges. These include monitoring, reporting, planning, and problem analysis of past and current air traffic, e.g., to identify hotspots, minimize delays, or to optimize sector assignments to air-traffic controllers. To cope with these challenges, cyber worlds can be used for interactive visual analysis and analytical reasoning based on aircraft trajectory data. However, with growing data size and complexity, visualization requires high computational efficiency to process that data within real-time constraints. This paper presents a technique for real-time animated visualization of massive trajectory data. It enables (1) interactive spatio-temporal filtering, (2) generic mapping of trajectory attributes to geometric representations and appearance, and (3) real-time rendering within 3D virtual environments such as virtual 3D airport or 3D city models. Different visualization metaphors can be efficiently built upon this technique such as temporal focus+context, density maps, or overview+detail methods. As a general-purpose visualization technique, it can be applied to general 3D and 3+1D trajectory data, e.g., traffic movement data, geo-referenced networks, or spatio-temporal data, and it supports related visual analytics and data mining tasks within cyber worlds.     

A multiscale urban complexity index based on 3D wavelet transform for spectral–spatial feature extraction and classification: an evaluation on the 8-channel WorldView-2 imagery

The three-dimensional wavelet transform (3D-WT) processes a multispectral remotely sensed image as a cube and hence it is able to simultaneously represent variation information in joint spectral–spatial feature space. The urban complexity index (UCI) built on the 3D-WT is defined by comparing the amount of spectral and spatial variation, since natural features have relatively smaller spatial changes than spectral changes but urban areas show more variation in the spatial domain. The calculation of the UCI is subject to the selection of window sizes; therefore, in this study, a multiscale UCI (M-UCI) is proposed by integrating the UCI features in different moving windows and decomposition levels. The performance of the M-UCI was evaluated on two WorldView-2 data sets over urban and suburban areas, respectively. Experimental results showed that the M-UCI was effective in integrating multiscale information contained in different windows and gave higher accuracies than the single-scale UCI. In experiments, the proposed M-UCI was compared with a pixel shape index (PSI), which is a texture measure extracted from the spatial domain alone. It was revealed that the PSI was more effective for the classification of urban areas than natural landscapes, whereas the M-UCI was applicable for both urban and natural areas since it represented the joint spectral–spatial domains.     

Development of a novel system to quantify the spatial–temporal parameters for crutch-assisted quadrupedal gait

Abstract

Patients with gait disorders often use bilateral crutches along with their own two legs. It is a kind of quadrupedalism. Crutch-assisted gait is usually described and evaluated qualitatively. In this study, we developed a system to quantify the spatial and temporal parameters for crutch-assisted quadrupedalism. Our system consists of walkway hardware and our originally developed software. We specifically extended the measurable area to 1200 mm × 4800 mm, large enough to measure crutch gait. Using our system, we could describe crutch gait precisely. Our system has a capability to evaluate differences between patients and changes within a patient.     

A lightweight model with spatial–temporal correlation for cellular traffic prediction in Internet of Things

The Journal of Supercomputing - Accurate cellular traffic prediction becomes more and more critical for efficient network resource management in the Internet of Things (IoT). However, high-accuracy...     

Spectral–spatial based sub-pixel mapping of remotely sensed imagery with multi-scale spatial dependence

Sub-pixel mapping (SPM) is a technique used to obtain a land-cover map with a finer spatial resolution than input remotely sensed imagery. Spectral–spatial based SPM can directly apply original remote-sensing images as input to produce fine-resolution land-cover maps. However, the existing spectral–spatial based SPM algorithms only use the maximal spatial dependence principle (calculated at the sub-pixel scale) as the spatial term to describe the local spatial distribution of different land-cover features, which always results in an over-smoothed and discontinuous land-cover map. The spatial dependence can also be calculated at the coarse-pixel scale to maintain the holistic land-cover pattern information of the resultant fine-resolution land-cover map. In this article, a novel spectral–spatial based SPM algorithm with multi-scale spatial dependence is proposed to overcome the limitation in the existing spectral–spatial based SPM algorithms. The objective function of the proposed SPM algorithm is composed of three parts, namely spectral term, sub-pixel scale based spatial term, and coarse-pixel scale based spatial term. Synthetic multi-spectral, degraded Landsat multi-spectral and real IKONOS multi-spectral images are employed in the experiments to validate the performance of the proposed SPM algorithm. The proposed algorithm is evaluated visually and quantitatively by comparing with the hard-classification method and two traditional SRM algorithms including pixel-swapping (PS) and Markov-random-field (MRF) based SPM. The results indicate that the proposed algorithm can generate fine-resolution land-cover maps with higher accuracies and more detailed spatial information than other algorithms.     

Optical spatial image processor based on aliasing of?pseudo-periodic sampling

In this paper, we present a new configuration for a real-time spatial image processor that is based upon a spatially incoherent imaging setup in which a grating is attached to the object plane. By proper adjusting of the magnification of the imaging system to the spatial period of the grating and the sampling grid of the camera, the aliasing effect along the non-uniform digital sampling realizes a tunable spectral distribution that is applied over the spectrum of the object. Preliminary numerical demonstration of the operation principle is provided.     

Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai–Tibetan Plateau

Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai–Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVC_UAV) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVC_ground), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R²) between FVC_UAV and vegetation indices (VIs) was significantly greater than that between FVC_ground and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods.     

Mutual information-based selection of optimal spatial–temporal patterns for single-trial EEG-based BCIs

The common spatial pattern (CSP) algorithm is effective in decoding the spatial patterns of the corresponding neuronal activities from electroencephalogram (EEG) signal patterns in brain–computer interfaces (BCIs). However, its effectiveness depends on the subject-specific time segment relative to the visual cue and on the temporal frequency band that is often selected manually or heuristically. This paper presents a novel statistical method to automatically select the optimal subject-specific time segment and temporal frequency band based on the mutual information between the spatial–temporal patterns from the EEG signals and the corresponding neuronal activities. The proposed method comprises four progressive stages: multi-time segment and temporal frequency band-pass filtering, CSP spatial filtering, mutual information-based feature selection and naïve Bayesian classification. The proposed mutual information-based selection of optimal spatial–temporal patterns and its one-versus-rest multi-class extension were evaluated on single-trial EEG from the BCI Competition IV Datasets IIb and IIa respectively. The results showed that the proposed method yielded relatively better session-to-session classification results compared against the best submission.     

Endogenous brain–machine interface based on the correlation of EEG maps

In this paper, a non-invasive endogenous brain–machine interface (BMI) based on the correlation of EEG maps has been developed to work in real-time applications. The classifier is able to detect two mental tasks related to motor imagery with good success rates and stability. The BMI has been tested with four able-bodied volunteers. First, the users performed a training with visual feedback to adjust the classifier. Afterwards, the users carried out several trajectories in a visual interface controlling the cursor position with the BMI. In these tests, score and accuracy were measured. The results showed that the participants were able to follow the targets during the performed trajectory, proving that the EEG mapping correlation classifier is ready to work in more complex real-time applications aimed at helping people with a severe disability in their daily life.     

EMERS: a tree matching–based performance evaluation of mathematical expression recognition systems

Performance evaluation of mathematical expression recognition systems is attempted. The proposed method assumes expressions (input as well as recognition output) are coded following MathML or T_EX/LaT_EX (which also gets converted into MathML) format. Since any MathML representation follows a tree structure, evaluation of performance has been modeled as a tree-matching problem. The tree corresponding to the expression generated by the recognizer is compared with the groundtruthed one by comparing the corresponding Euler strings. The changes required to convert the tree corresponding to the expression generated by the recognizer into the groundtruthed one are noted. The number of changes required to make such a conversion is basically the distance between the trees. This distance gives the performance measure for the system under testing. The proposed algorithm also pinpoints the positions of the changes in the output MathML file. Testing of the proposed evaluation method considers a set of example groundtruthed expressions and their corresponding recognized results produced by an expression recognition system.