Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation

Region‐growing segmentation algorithms are useful for remote sensing image segmentation. These algorithms need the user to supply control parameters, which control the quality of the resulting segmentation. An objective function is proposed for selecting suitable parameters for region‐growing algorithms to ensure best quality results. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighbourhood. The measure combines a spatial autocorrelation indicator that detects separability between regions and a variance indicator that expresses the overall homogeneity of the regions.     

Spatial–spectral ant colony optimization for hyperspectral image classification

Hyperspectral satellite images contain a lot of information in terms of spectral behaviour of objects and this information can be extracted by several mechanisms including image classification. Traditional spectral information-based methods of hyperspectral image classification are generally followed by spatial information-driven post-processing techniques such as relaxation labelling and Markov Random Field. Spectral or spatial information alone may lead to different results depending upon scene captured. An algorithm which can incorporate influence of both spectral and spatial features is needed to address this problem. In this article, an ant colony optimisation-based hyperspectral image classification technique is proposed. This method exploits both spatial and spectral features. Five standard hyperspectral data sets have been used to validate the proposed method and comparisons with other approaches have been carried out. It was observed that the proposed method yielded a significant improvement in classification accuracy. For the instance, nearly 10% increase in accuracy was observed when compared to Support Vector Machine for Indian pines, Botswana, and Salinas images.     

Spatial–spectral locality constrained elastic net hypergraph for hyperspectral image clustering

Hypergraph is an effective method used to represent the contextual correlation within hyperspectral imagery for clustering. Nevertheless, how to discover the closely correlated samples to form hyperedges is the key issue for constructing an informative hypergraph. In this article, a new spatial–spectral locality constrained elastic net hypergraph learning model is proposed for hyperspectral image clustering (i.e. unsupervised classification). In order to utilize the spatial–spectral correlation among the pixels in hyperspectral images, first, we construct a locality-constrained dictionary by selecting K relevant pixels within a spatial neighbourhood, which activates the most correlated atoms and suppresses the uncorrelated ones. Second, each pixel is represented as a linear combination of the atoms in the dictionary under the elastic net regularization. Third, based on the obtained representations, the pixels and their most related pixels are linked as hyperedges, which can effectively capture high–order relationships among the pixels. Finally, a hypergraph Laplacian matrix is built for unsupervised learning. Experiments have been conducted on two widely used hyperspectral images, and the results show that the proposed method can achieve a superior clustering performance when compared to state-of-the-art methods.     

Parallel spatiotemporal autocorrelation and visualization system for large-scale remotely sensed images

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering.     

ELM-based spectral–spatial classification of hyperspectral images using extended morphological profiles and composite feature mappings

Extreme Learning Machine (ELM) is a supervised learning technique for a class of feedforward neural networks with random weights that has recently been used with success for the classification of hyperspectral images. In this work, we show that the morphological techniques can be integrated in this kind of classifiers using several composite feature mappings which are proposed for ELM. In particular, we present a spectral–spatial ELM-based classifier for hyperspectral remote-sensing images that integrates the information provided by extended morphological profiles. The proposed spectral–spatial classifier allows different weights for both spatial and spectral features, outperforming other ELM-based classifiers in terms of accuracy for land-cover applications. The accuracy classification results are also better than those obtained by equivalent spectral–spatial Support-Vector-Machine-based classifiers.     

Approximation-based feature selection and application for algae population estimation

This paper presents a data-driven approach for feature selection to address the common problem of dealing with high-dimensional data. This approach is able to handle the real-valued nature of the domain features, unlike many existing approaches. This is accomplished through the use of fuzzy-rough approximations. The paper demonstrates the effectiveness of this research by proposing an estimator of algae populations, a system that approximates, given certain water characteristics, the size of algae populations. This estimator significantly reduces computer time and space requirements, decreases the cost of obtaining measurements and increases runtime efficiency, making itself more viable economically. By retaining only information required for the estimation task, the system offers higher accuracy than conventional estimators. Finally, the system does not alter the domain semantics, making any distilled knowledge human-readable. The paper describes the problem domain, architecture and operation of the system, and provides and discusses detailed experimentation. The results show that algae estimators using a fuzzy-rough feature selection step produce more accurate predictions of algae populations in general.     

Bidirectional effect on a spectral image sensor for in‐field crop reflectance assessment

Varying illumination geometry affects spectral measurements of a target reflectance and the intensity of solar radiation is the most important factor for in‐field spectral measurements. This paper reports the effect of bidirectional electromagnetic radiation on an image‐based reflectance sensor designed for plant nitrogen assessment. The results show the nonlinearity of reflectance as a function of the solar zenith angle. Ambient illumination was analysed and compensated for using fixed nadir‐view positions of a solar radiometer and a 3‐charge‐coupled device (CCD) multispectral imaging sensor (MSIS). A compensation algorithm was developed to correct for the nonlinearity of both sensors. The compensated reflectance remained linearly consistent with varying the solar zenith angle throughout the daytime within a maximum standard deviation of 0.62% at all three (green, red and near‐infrared) spectral channels, when testing with a 20% reflectance panel. The consistent reflectance was recovered under both sunny and cloudy conditions.     

Automatic spatial analysis and pedestrian flow control for real-time crowd simulation in an urban environment

Distributing a collection of virtual humans throughout a large urban environment, where limited semantic information is available, poses a problem when attempting to create a visually realistic real-time environment. Randomly positioning agents within an urban environment will not cover the environment with virtual humans in a plausible way. For example, areas of the urban space that are more frequently used should have a higher population density both at the start and during the simulation. It is infeasible to manually identify all the areas in the urban environment which should be crowded or sparsely populated when considering a scalable method, suitable for large environments. Consequently, this paper combines and extends techniques from spatial analysis and virtual agent behaviour simulations to develop a system capable of automatically distributing pedestrians in an urban environment. In particular, it extends the Point-Based Space Syntax technique to enable the automatic analysis of a large urban environment in the presence of limited contextual information. This analysis specifies a set of population densities for areas in the environment and these values are used to initialise the locations of all the virtual humans in the environment. In addition to the initialisation stage the population densities in each area are consulted to ensure that the correct distribution of virtual humans is maintained throughout the simulation. The system is tested on an arbitrary section of a real city and comparisons of the characteristic parts of the test environment are correlated with the pedestrian movements.

A generative framework for fast urban labeling using spatial and temporal context

This paper introduces a multi-level classification framework for the semantic annotation of urban maps as provided by a mobile robot. Environmental cues are considered for classification at different scales. The first stage considers local scene properties using a probabilistic bag-of-words classifier. The second stage incorporates contextual information across a given scene (spatial context) and across several consecutive scenes (temporal context) via a Markov Random Field (MRF). Our approach is driven by data from an onboard camera and 3D laser scanner and uses a combination of visual and geometric features. By framing the classification exercise probabilistically we take advantage of an information-theoretic bail-out policy when evaluating class-conditional likelihoods. This efficiency, combined with low order MRFs resulting from our two-stage approach, allows us to generate scene labels at speeds suitable for online deployment. We demonstrate the virtue of considering such spatial and temporal context during the classification task and analyze the performance of our technique on data gathered over almost 17 km of track through a city.     

A large-scale distributed framework for information retrieval in large dynamic search spaces

One of the main problems facing human analysts dealing with large amounts of dynamic data is that important information may not be assessed in time to aid the decision making process. We present a novel distributed processing framework called Intelligent Foraging, Gathering and Matching (I-FGM) that addresses this problem by concentrating on resource allocation and adapting to computational needs in real-time. It serves as an umbrella framework in which the various tools and techniques available in information retrieval can be used effectively and efficiently. We implement a prototype of I-FGM and validate it through both empirical studies and theoretical performance analysis.     

Spectral entropy and spectral shape based pre-quantization for real time speaker identification system

Pre-processing is one of the vital steps for developing robust and efficient recognition system. Better pre-processing not only aid in better data selection but also in significant reduction of computational complexity. Further an efficient frame selection technique can improve the overall performance of the system. Pre-quantization (PQ) is the technique of selecting less number of frames in the pre-processing stage to reduce the computational burden in the post processing stages of speaker identification (SI). In this paper, we develop PQ techniques based on spectral entropy and spectral shape to pick suitable frames containing speaker specific information that varies from frame to frame depending on spoken text and environmental conditions. The attempt is to exploit the statistical properties of distributions of speech frames at the pre-processing stage of speaker recognition. Our aim is not only to reduce the frame rate but also to maintain identification accuracy reasonably high. Further we have also analyzed the robustness of our proposed techniques on noisy utterances. To establish the efficacy of our proposed methods, we used two different databases, POLYCOST (telephone speech) and YOHO (microphone speech).     

Use of spatial structure analysis of hyperspectral data cubes for detection of insect‐induced stress in wheat plants

Wheat plants were experimentally infested with wheat stem sawflies, and hyperspectral images (reflectance range from 402.8–838.7 nm) were collected from leaves of infested and non‐infested plants. Mean and variance reflectance per leaf were calculated in five of 213 spectral bands (452, 553, 657, 725, and 760 nm) and compared with vegetation indices (NDVI, SI and PRI), and standard variogram parameters (nugget, sill and range values). Mean reflectance values and their variance values and vegetation indices showed significant effects of sawfly infestation in one dataset but not in another. Based on directional variogram analyses, we showed that: (1) better separation of leaf type and infested/non‐infested wheat plants was seen in variograms in longitudinal direction compared to transverse; (2) mainly spectral bands in the red edge and NIR showed consistent effect of sawfly infestation; (3) range values were not affected significantly by either sawfly infestation or leaf type; and (4) sawfly‐induced stress was most likely to be detected about three weeks after infestation. Variogram analysis is one of the key standards in quantitative spatial ecology, and this study supports further research into its use in remote sensing with particular emphasis on detection of biotic stress.     

Movement prediction based cooperative caching for location dependent information service in mobile ad hoc networks

Location Dependent Information Services (LDISs), through which mobile clients can access location sensitive data such as weather information, traffic reports, and local news, are gaining increasing popularity in recent years. Due to limited client power and intermittent connectivity, caching is an important approach to improve the performance of LDISs. In this paper, we propose a cache replacement policy called Location Dependent Cooperative Caching (LDCC). Unlike existing location dependent cache replacement policies, the LDCC strategy applies a prediction model to approximate client movement behavior and a probabilistic transition model to analyze the communication cost. These models are used in the design of a cache replacement policy to improve system overall performance. Simulation results demonstrate that the proposed strategy significantly outperforms existing caching policies in providing LDIS in mobile ad hoc networks.     

Efficient selection strategies towards processor reordering techniques for improving data locality in heterogeneous clusters

Grid architecture integrates geographically distributed nodes to manage and provide resources to execute scientific applications. For data locality, applications with different computational phases require data redistribution for realignment. The tradeoff between high efficiency computation and communication cost of data redistribution accompanies. This paper introduces a research model and two methods to derive new lists of processor logical id according to the characteristics of heterogeneous network. Both methods provide choices of more low-cost communication schedules in grid. The simulations show both proposed methods yield outstanding performance in grid.     

A multiplierless pruned DCT-like transformation for image and video compression that requires ten additions only

A multiplierless pruned approximate eight-point discrete cosine transform (DCT) requiring only ten additions is introduced. The proposed algorithm was assessed in image and video compression, showing competitive performance with state-of-the-art methods. Digital synthesis in 45 nm CMOS technology up to place-and-route level indicates clock speed of 288 MHz at a 1.1 V supply. The \(8\times 8\) block rate is 36 MHz. The DCT approximation was embedded into HEVC reference software; resulting video frames, at up to 327 Hz for 8-bit RGB HEVC, presented negligible image degradation.     

Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery

Textural and local spatial statistical information is important in the classification of urban areas using very high resolution imagery. This paper describes the utility of textural and local spatial statistics for the improvement of object‐oriented classification for QuickBird imagery. All textural/spatial bands were used as additional bands in the supervised object‐oriented classification. The texture analysis is based on two levels: segmented image objects and moving windows across the whole image. In the texture analysis over image objects, the angular second moment textural feature at a 45° angle showed an improved classification performance with regard to buildings, depicting the patterns of buildings better than any other directions. The texture analysis based on moving windows across the whole image was conducted with various window sizes (from 3×3 to 13×13), and four grey‐level co‐occurrence matrix (GLCM) textural features (homogeneity, contrast, angular second moment, and entropy) were calculated. The contrast feature with the 7×7 window size improved classification up to 6%. One type of local spatial statistics, Moran's I feature with the vertical neighbourhood rule, improved the classification accuracy even further, up to 7%. Comparison of results between spectral and spectral+textural/spatial information indicated that textural and spatial information can be used to improve the object‐oriented classification of urban areas using very high resolution imagery.     

A framework for development and evaluation of a dynamic subchannel allocation scheme in an OFDMA system

This paper presents a framework for allocating radio resources to the Access Points (APs) introducing an Access Point Controller (APC). Radio resources can be either time slots or subchannels. The APC assigns subchannels to the APs using a dynamic subchannel allocation scheme. The developed framework evaluates the dynamic subchannel allocation scheme for a downlink multicellular Orthogonal Frequency Division Multiple Access (OFDMA) system. In the considered system, each AP and the associated Mobile Terminals (MTs) are not operating on a frequency channel with fixed bandwidth, rather the channel bandwidth for each AP is dynamically adapted according to the traffic load. The subchannels assignment procedure is based on quality estimations due to the interference measurements and the current traffic load. The traffic load estimation is realized with the measurement of the utilization of the assigned radio resources. The reuse partitioning for the radio resources is done by estimating mutual Signal to Interference Ratio (SIR) of the APs. The developed dynamic subchannel allocation ensures Quality of Service (QoS), better traffic adaptability, and higher spectrum efficiency with less computational complexity.