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With the fast advancement of remote sensing platforms and sensors, remotely sensed imagery (RSI) is increasingly being characterized by both high spatial resolution and high temporal resolution. How to efficiently use the rich spatial and temporal information in RSI for highly accurate object detection and classification is an important research question. Nevertheless, there is still a lack of a probabilistic framework that is capable of fully accounting for the spatial–temporal information in RSI for improved applications. In this paper, we present a Bayesian spatial–temporal random field model that constitutes a complete probabilistic framework for fully explaining the spatial–temporal correlation in RSI, leading to an enhanced object detection approach that is used for cloud detection from RSI. Under the Bayesian theorem, the posterior distribution of a label field is decomposed into the label prior, the data likelihood, the temporal label likelihood, and the temporal data likelihood. To address the difficulties in modeling the complex spatial–temporal correlation effect in the temporal data likelihood, a stochastic sampling approach is presented. Based on the maximum a posteriori approach, the posterior distribution is seamlessly integrated into the graph-cut optimization framework, and, therefore, the model optimization can be efficiently solved. The proposed algorithm is tested for cloud detection on both simulated and real RSIs and the results demonstrate that the proposed algorithm can effectively exploit the spatial–temporal information for achieving higher detection accuracy. [ABSTRACT FROM PUBLISHER]

A novel model is presented to address the problem of semantic clustering of geo-objects in very high resolution panchromatic satellite images. The proposed model combines a probabilistic topic model with a multiscale image representation into an automatic framework by embedding both document and scale selections. The probabilistic topic model is used to characterize the statistical distributions of both intraclass appearance and inter-class coherence of geo-objects within documents, i.e., squared sub-images. Because the bag-of-words assumption involved in the probabilistic topic models does not consider the spatial coherence between topic labels, the multiscale image representation is designed to provide a self-adaptive spatial regularization for various geo-object categories. By introducing scale and document selections, the automatic framework integrates the probabilistic topic model and the multiscale image representation to ensure that words on a site should be allocated the same topic label no matter what documents they reside in. Consequently, unlike the traditional method of applying topic models for analyzing satellite images, the process of explicitly generating a set of documents before modeling and then combining multiple labels for a word on a given site is unnecessary. Gibbs sampling is adopted for parameter estimation and image clustering. Extensive experimental evaluations are designed to first analyze the effect of parameters in the proposed model and then compare the results of our model with those of some state-of-the-art methods for three different types of images. The results indicate that the proposed algorithm consistently outperforms these exiting state-of-the-art methods in all of the experiments. [ABSTRACT FROM PUBLISHER]

Abstract: Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels in an image located near texture boundaries have a tendency to be misclassified. Boundary preservation when using the GLCP texture features for image segmentation is important. An advancement which exploits spatial relationships has been implemented. The generated features are referred to as weighted GLCP (WGLCP) texture features. In addition, an investigation for selecting suitable GLCP parameters for improved boundary preservation is presented. From the tests, WGLCP features provide improved boundary preservation and segmentation accuracy at a computational cost. As well, the GLCP correlation statistical parameter should not be used when segmenting images with high contrast texture boundaries. [Copyright &y& Elsevier]

A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. The fused feature set utilizes both the Gabor filter's capability of accurately capturing lower and mid-frequency texture information and the GLCP's capability in texture information relevant to higher frequency components. Evaluation methods include comparing feature space separability and comparing image segmentation classification rates. The fused feature sets are demonstrated to produce higher feature space separations, as well as higher segmentation accuracies relative to the individual feature sets. Fused feature sets also outperform individual feature sets for noisy images, across different noise magnitudes. The curse of dimensionality is demonstrated not to affect segmentation using the proposed the 48-dimensional fused feature set. Gabor magnitude responses produce higher segmentation accuracies than linearly normalized Gabor magnitude responses. Feature reduction using principal component analysis is acceptable for maintaining the segmentation performance, but feature reduction using the feature contrast method dramatically reduced the segmentation accuracy. Overall, the designed fused feature set is advocated as a means for improving texture segmentation performance. [ABSTRACT FROM AUTHOR]

Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate east squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features. [ABSTRACT FROM AUTHOR]

This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures. [ABSTRACT FROM AUTHOR]

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL.The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4% (σ=3.08%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method. [Copyright &y& Elsevier]

Abstract: The acquisition of laser range measurements can be a time consuming process for situations where high spatial resolution is required. As such, optimizing the acquisition mechanism is of high importance for many range measurement applications. Acquiring such data through a dynamically small subset of measurement locations can address this problem. In such a case, the measured information can be regarded as incomplete, which necessitates the application of special reconstruction tools to recover the original data set. The reconstruction can be performed based on the concept of sparse signal representation. Recovering signals and images from their sub-Nyquist measurements forms the core idea of compressive sensing (CS). A new saliency-guided CS-based algorithm for improving the reconstruction of range image from sparse laser range measurements has been developed. This system samples the object of interest through an optimized probability density function derived based on saliency rather than a uniform random distribution. Particularly, we demonstrate a saliency-guided sampling method for simultaneously sensing and coding range image, which requires less than half the samples needed by conventional CS while maintaining the same reconstruction performance, or alternatively reconstruct range image using the same number of samples as conventional CS with a 16dB improvement in signal-to-noise ratio. For example, to achieve a reconstruction SNR of 30dB, the saliency-guided approach required 30% of the samples in comparison to the standard CS approach that required 90% of the samples in order to achieve similar performance. [Copyright &y& Elsevier]

Abstract: In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors such as human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features such as flow vectors or control points, this paper builds upon a recently introduced approach that makes use of higher-level features of intentionality. Individuals in the scene are modelled as intentional agents, and unusual behaviour is detected by evaluating the explicability of the agent''s trajectory with respect to known spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach. [Copyright &y& Elsevier]