Accurate blood glucose prediction is vital for effective diabetes management. However, traditional models focus primarily on improving accuracy, often overlooking the importance of quantifying uncertainty, which is essential for informed clinical decision-making. This study presents an uncertainty-aware blood glucose prediction model using a Bayesian neural network, which not only focuses on predictive accuracy but also emphasizes uncertainty analysis. The model is trained on blood glucose data and evaluated using Root Mean Squared Error (RMSE) for accuracy and confidence interval coverage to assess uncertainty calibration. The results show that the model achieves an RMSE of 22.21, indicating strong predictive performance. Moreover, the observed glucose values consistently fall within the predicted confidence intervals$(\pm 2 \sigma)$, demonstrating well-calibrated uncertainty estimates. The low correlation (0.06) between glucose rate of change and uncertainty further suggests that the model performs robustly across both stable and fluctuating glucose levels.

As an essential procedure in earth observation system, change detection (CD) aims to reveal the spatial-temporal evolution of the observation regions. A key prerequisite for existing change detection algorithms is aligned geo-references between multi-temporal images by fine-grained registration. However, in the majority of real-world scenarios, a prior manual registration is required between the original images, which significantly increases the complexity of the CD workflow. In this paper, we proposed a self-supervision motivated CD framework with geometric estimation, called “MatchCD”. Specifically, the proposed MatchCD framework utilizes the zero-shot capability to optimize the encoder with self-supervised contrastive representation, which is reused in the downstream image registration and change detection to simultaneously handle the bi-temporal unalignment and object change issues. Moreover, unlike the conventional change detection requiring segmenting the full-frame image into small patches, our MatchCD framework can directly process the original large-scale image (e.g., 6K× 4Kresolutions) with promising performance. The performance in multiple complex scenarios with significant geometric distortion demonstrates the effectiveness of our proposed framework.

In conventional remote sensing change detection (RSCD) procedures, extensive manual labeling for bi-temporal images is first required to maintain the performance of subsequent fully supervised training. However, pixel-level labeling for change detection (CD) tasks is very complex and time-consuming. In this article, we explore a novel self-supervised contrastive framework applicable to the RSCD task, which promotes the model to accurately capture spatial, structural, and semantic information through the domain adapter (DA) and the hierarchical contrastive head. The proposed SSLChange framework accomplishes self-learning only by taking a single-temporal sample and can be flexibly transferred to mainstream CD baselines. With self-supervised contrastive learning, feature representation pretraining can be performed directly based on the original data even without labeling. After a certain number of labels are subsequently obtained, the pretrained features will be aligned with the labels for fully supervised fine-tuning. Without introducing any additional data or labels, the performance of downstream baselines will experience a significant enhancement. Experimental results on two entire datasets and six diluted datasets show that our proposed SSLChange improves the performance and stability of CD baseline in data-limited situations. The code of SSLChange is available athttps://github.com/MarsZhaoYT/SSLChange

The problem of unveiling the author of a given text document from multiple candidate authors is called authorship attribution. Manifold word-based stylistic markers have been successfully used in deep learning methods to deal with the intrinsic problem of authorship attribution. Unfortunately, the performance of word-based authorship attribution systems is limited by the vocabulary of the training corpus. Literature has recommended character-based stylistic markers as an alternative to overcome the hidden word problem. However, character-based methods often fail to capture the sequential relationship of words in texts which is a chasm for further improvement. The question addressed in this paper is whether it is possible to address the ambiguity of hidden words in text documents while preserving the sequential context of words. Consequently, a method based on bidirectional long short-term memory (BLSTM) with a 2-dimensional convolutional neural network (CNN) is proposed to capture sequential writing styles for authorship attribution. The BLSTM was used to obtain the sequential relationship among characteristics using subword information. The 2-dimensional CNN was applied to understand the local syntactical position of the style from unlabeled input text. The proposed method was experimentally evaluated against numerous state-of-the-art methods across the public corporal of CCAT50, IMDb62, Blog50, and Twitter50. Experimental results indicate accuracy improvement of 1.07%, and 0.96%, on CCAT50 and Twitter, respectively, and produce comparable results on the remaining datasets.

Substantial progress has been made in detecting ships of arbitrary orientation in synthetic aperture radar (SAR) images. However, the mainstream method is still limited by the horizontal bounding box (HBB) boundary, which cannot provide scaling information for the length and width of the oriented bounding box (OBB) in an intuitive way. In this study, we propose a novel encode representation to describe the OBB by breaking through the border restriction of the HBB. Specifically, we derive an inclination factor from two left-top point offsets (LTPO), which enables us to directly infer the coordinates of the four OBB vertices and obtain an oriented rectangular proposal. To obtain high-quality oriented semantic features, we utilize a feature adaptive module (FAM) to learn the shape and orientation implied by arbitrary-oriented ships through spatial transformation. Our comparative experiments demonstrate that our proposed method achieves superior performance and detection accuracy on two commonly-used benchmark datasets for oriented SAR ship detection, namely SSDD and HRSID.

Object detection in remote sensing images relies on a large amount of labeled data for training. However, the increasing number of new categories and class imbalance make exhaustive annotation impractical. Few-shot object detection (FSOD) addresses this issue by leveraging meta-learning on seen base classes and fine-tuning on novel classes with limited labeled samples. Nonetheless, the substantial scale and orientation variations of objects in remote sensing images pose significant challenges to existing few-shot object detection methods. To overcome these challenges, we propose integrating a feature pyramid network and utilizing prototype features to enhance query features, thereby improving existing FSOD methods. We refer to this modified FSOD approach as a Strong Baseline, which has demonstrated significant performance improvements compared to the original baselines. Furthermore, we tackle the issue of spatial misalignment caused by orientation variations between the query and support images by introducing a Transformation-Invariant Network (TINet). TINet ensures geometric invariance and explicitly aligns the features of the query and support branches, resulting in additional performance gains while maintaining the same inference speed as the Strong Baseline. Extensive experiments on three widely used remote sensing object detection datasets, i.e., NWPU VHR-10.v2, DIOR, and HRRSD demonstrated the effectiveness of the proposed method.

Relative radiometric normalization (RRN) is widely used for radiometric calibration of bitemporal multispectral images prior to any temporal analysis such as change detection. However, standard RRN methods are not robust against anomalous (or changed) pixels, which warp the calibration and decrease the spectral similarity of processed images. This letter proposes a novel random sample consensus-based RRN method, which only uses small pixel subsets to implement the linear mapping relationship for RRN, and does not require the calibration of its parameters. The experimental results show that the proposed method performs favorably against the widely used RRN methods in all metrics considered in this letter.

Vehicle detection in aerial images has a wide range of applications, and the majority of vehicle detection methods use the bounding-box approach for localization. But the bounding-box approach usually yields low precision and recall rates, especially under the dense vehicle density situations where the close spatial proximity of the vehicles confuses the bounding-box detectors. This letter proposes a Fully Convolutional Lightweight Pyramid Network (FCLPN) to detect vehicles in visible-spectrum aerial images. Unlike the bounding-box approach, FCLPN performs pixel-level localization and classification. FCLPN trained on the DLR-3K dataset is directly tested on DLR-3K, VEDAI, COWC, and VAID datasets to validate its generalization strength. Experimental results show that FCLPN performs better than state-of-the-art methods in aerial vehicle detection in terms of Precision, F1 score, and mAP.

This letter presents a new feature pyramid network (FPN) called the gated ladder-shaped FPN (GLFPN) to construct more representative feature pyramids for detecting objects of different sizes in optical remote sensing images. We first use convolution and concatenation operations to fuse three base features extracted by a ResNet backbone. We then obtain multilevel features from these base features. Finally, we use a selective gate to fuse features from multiple levels with equivalent sizes. To evaluate the effectiveness of the proposed GLFPN, we integrate it into the RetinaNet architecture by replacing the conventional FPN. The experimental results on two optical remote sensing image data sets show that the proposed method outperforms the methods compared in this letter.

Remote sensing scene classification is a complex task due to large intraclass variations in object appearances with a small number of samples per class and high interclass similarities due to shared objects in different classes, which usually cause model overfitting and high interclass confusion. To address these challenges, a multiple supervision approach, called multiple supervision network (MSNet), consisting of the ResNet-50 backbone, a feature discriminative branch (FDB), and a feature confusion branch (FCB) is proposed in this letter. The FDB selects discriminative features per class and suppresses peaks in feature maps to examine more informative regions with lower feature magnitudes. Meanwhile, the FCB reduces overfitting by introducing confusion to the input of a fully connected layer which also enhances the robust features. The FDB and FCB are only used in training of the backbone and not used in inference. Thus, the proposed method does not introduce additional computing time on the backbone while it significantly boosts its performance in scene classification. The experimental results show that MSNet outperforms the methods considered in this letter.

Unlike optical sensors, Synthetic Aperture Radar (SAR) sensors acquire images of the Earth’s surface with all-weather and all-time capabilities, which is vital in a situation such as a disaster assessment. However, SAR sensors do not offer as rich visual information as optical sensors. SAR-to-Optical image-to-image translation generates optical images from SAR images to benefit from what both imaging modalities have to offer. It also enables multi-sensor image analysis of the same scene for applications such as heterogeneous change detection. Various architectures of Generative Adversarial Networks (GANs) have achieved remarkable image-to-image translation results in different domains. Still, their performances in SAR-to-Optical image translation have not been analyzed in the remote sensing domain. This paper compares and analyses the state-of-the-art GAN-based translation methods with open-source implementations for SAR-to-Optical image translation. The results show that GAN-based SAR-to-Optical image translation methods achieve satisfactory results. However, their performances depend on the structural complexity of the observed scene and the spatial resolution of the data. We also introduce a new dataset with a higher resolution than the existing SAR-to-Optical image datasets and release implementations of GAN-based methods considered in this paper to support the reproducible research in remote sensing.

Arbitrary-oriented remote sensing object detection is a challenging task because of the periodicity of the angle and large variety of aspect ratios. To address these issues, this letter proposes two simple yet powerful methods: a variant Gaussian label (VGL) generating method and a channel-wise pixel attention (CPA) module. Specifically, VGL is generated to handle the periodicity of the angle and produce various labels to adapt diverse aspect ratios of objects. Then, CPA is utilized to fuse features between channels and pixels, to obtain a global receptive field and extract more robust features. In addition, we collect a dataset, namely, Northwestern Polytechnical University Very-High-Resolution (NWPU VHR-10-R), which is relabeled with oriented bounding boxes based on NWPU VHR-10. To evaluate our proposed method, experiments are conducted on several publicly available benchmark datasets, including the High Resolution Ship Collections 2016 (HRSC2016) and NWPU VHR-10-R. Experimental results show that our method achieves substantial gains compared with baseline approaches.

In this article, we propose a novel framework to radiometrically correct unregistered multisensor image pairs based on the extracted feature points with the KAZE detector and the conditional probability (CP) process in the linear model fitting. In this method, the scale, rotation, and illumination invariant radiometric control set samples (SRII-RCSS) are first extracted by the blockwise KAZE strategy. They are then distributed uniformly over both textured and texture-less land use/land cover (LULC) using grid interpolation and a set of nearest-neighbors. Subsequently, SRII-RCSS are scored by a similarity measure, and the histogram of the scores is then used to refine SRII-RCSS. The normalized subject image is produced by adjusting the subject image to the reference image using the CP-based linear regression (CPLR) based on the optimal SRII-RCSS. The registered normalized image is finally generated by registration of the normalized subject image to the reference image through a two-pass registration method, namely affine-B-spline and, then, it is enhanced by updating the normalization coefficient of CPLR based on the SRII-RCSS. In this study, eight multitemporal data sets acquired by inter/intra satellite sensors were used in tests to comprehensively assess the efficiency of the proposed method. Experimental results show that the proposed method outperforms the existing state-of-the-art relative radiometric normalization (RRN) methods both qualitatively and quantitatively, indicating its capability for RRN of unregistered multisensor image pairs.

This article presents a new relative radiometric normalization (RRN) method for multitemporal satellite images based on the automatic selection and multistep optimization of the radiometric control set samples (RCSS). A novel image-fusion strategy based on the fast local Laplacian filter is employed to generate a difference index using the complementary information extracted from the change vector analysis and absolute gradient difference of the bitemporal satellite images. The difference index is then segmented into changed and unchanged pixels using a fast level-set method. A novel local outlier method is then applied to the unchanged pixels of the bitemporal images to identify the initial RCSS, which are then scored by a novel unchanged purity index, and the histogram of the scores is used to produce the final RCSS. The RRN between the bitemporal images is achieved by adjusting the subject image to the reference image using orthogonal linear regression on the final RCSS. The proposed method is applied to seven different data sets comprised of bitemporal images acquired by various satellites, including Landsat TM/ETM+, Sentinel 2B, Worldview 2/3, and Aster. The experimental results show that the method outperforms the state-of-the-art RRN methods. It reduces the average root-mean-square error (RMSE) of the best baseline method (IR-MAD) by up to 32% considering all data sets.

This paper presents a comprehensive survey of the state-of-the-art work on fault tolerance methods proposed for cloud computing. The survey classifies fault-tolerance methods into three categories: 1) ReActive Methods (RAMs); 2) PRoactive Methods (PRMs); and 3) ReSilient Methods (RSMs). RAMs allow the system to enter into a fault status and then try to recover the system. PRMs tend to prevent the system from entering a fault status by implementing mechanisms that enable them to avoid errors before they affect the system. On the other hand, recently emerging RSMs aim to minimize the amount of time it takes for a system to recover from a fault. Machine Learning and Artificial Intelligence have played an active role in RSM domain in such a way that the recovery time is mapped to a function to be optimized (i.e., by converging the recovery time to a fraction of milliseconds). As the system learns to deal with new faults, the recovery time will become shorter. In addition, current issues and challenges in cloud fault tolerance are also discussed to identify promising areas for future research.

Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well.

To perform contingent teaching and be responsive to students' needs during class, lecturers must be able to quickly assess the state of their audience. While effective teachers are able to gauge easily the affective state of the students, as class sizes grow this becomes increasingly difficult and less precise. The Wits Intelligent Teaching System (WITS) aims to assist lecturers with real-time feedback regarding student affect. The focus is primarily on recognising engagement or lack thereof. Student engagement is labelled based on behaviour and postures that are common to classroom settings. These proxies are then used in an observational checklist to construct a dataset of engagement upon which a CNN based on AlexNet is successfully trained and which significantly outperforms a Support Vector Machine approach. The deep learning approach provides satisfactory results on a challenging, real-world dataset with significant occlusion, lighting and resolution constraints.

A novel technique to generate the difference image (DI) in change detection analysis for polarimetric SAR (PolSAR) data is proposed. Unlike the standard methods, viz., band difference or intensity/amplitude ratioing, the proposed technique utilizes the full vector nature of multitemporal PolSAR data. In this data, a pixel is characterized by a 4 × 4 Kennaugh matrix. The geodesic distance (GD) on an unit sphere is utilized to define the distance between the Kennaugh matrices of the three elementary targets (trihedral, dihedral, and 45° rotated dihedral about the radar line of sight) producing canonical scattering mechanisms and the observed Kennaugh matrix. Three absolute differences of the GD from respective elementary targets are obtained for time instants t1and t2. The DI is then the maximum among the three quantities. The proposed technique is applied to two scenes obtained from the L-band UAVSAR data characterizing changes due to urbanization. The principal component analysis with k-means clustering proposed by Celik is used to obtain the binary change map. The proposed differencing method performs better than the single-channel intensity band ratio and the total power ratio for time instants t1and t2. The detection rate with the proposed technique is 6% and 20% better than the ratio methods for the two data sets, respectively, with the higher κ value as a measure of performance evaluation.

The maritime domain can be a very resource intensive region to actively monitor on a continuous basis. Remote sensing has been observed to be a valuable tool in this application, and the most effective strategy has been to supplement outputs from different Earth Observation tools in order to better classify target objects. The Visible Infrared Imaging Radiometer Suit is able to generate daily images of the earths surface spanning 2000 km; covering the large area of a country's Economic Exclusive Zone. The low level light sensitivity of the Day-Night band enable light sources above the oceans surface to be detected at night, appearing as a bright pixel against the darker ocean background. In this paper we investigate the application of an adaptive threshold method commonly used as a prescreening stage for ship detection in Synthetic Aperture Radar imagery, and compare it to a recently developed method specific to DNB imagery. Our initial application suggests the adaptive threshold method may be able to classify these unusually bright pixels at a slightly better false alarm rate when moon lit conditions are not as favourable and clouds cause an increase in false detections. It has been observed in previous experiments that one algorithm, or even a combination may not be enough to create a robust detection system and additional investigation is needed to develop proceeding stages to identify these false alarms and remove them.

Sparse representation is capable of modeling signals as linear combination of a few atoms from a pre-trained dictionary. It allows learning an adaptive dictionary that leads to highly sparse nature in the representation of signals. In this paper, sparse representation is deployed in a lossy hyperspectral data compression framework. Dictionaries that exploit spectral correlation, as well as both spectral and spatial correlations are trained using online dictionary learning. A hyperspectral data is then represented using the learned dictionary via sparse coding. The resulting sparse coefficients are encoded to formulate the final bit stream. Experimental results on a number of hyperspectral datasets show that the proposed approach is indeed competitive to wavelet based methods, such as 3D-SPIHT, in terms of rate-distortion performance.

This paper proposes a novel algorithm for global contrast enhancement using a new definition of spatial mutual information (SMI) of gray levels of an input image and PageRank algorithm. The gray levels are used to represent nodes in PageRank algorithm, and the weights between the nodes are computed according to their dependence and spatial spread over the image, which is quantified by using SMI. The rank vector of gray levels resulted from PageRank algorithm is used in mapping input gray levels to output. The damping factor of the PageRank algorithm is utilized to control the level of perceived global contrast on the output image. Furthermore, a new metric is proposed for image quality-aware relative contrast measurement between input and output images. Experimental results show that the proposed algorithm consistently produces good results.

Recently, computer vision‐based methods have started to replace conventional sensor‐based fire detection technologies. In general, visible band image sequences are used to automatically detect suspicious fire events in indoor or outdoor environments. There are several methods which aim to achieve automatic fire detection on visible band images, however, it is difficult to identify which method is the best performing as there is no fire image dataset which can be used to test the different methods. This study presents a benchmarking of state of the art wildland fire colour segmentation algorithms using a new fire dataset introduced for the first time. The dataset contains images of wildland fire in different contexts (fuel, background, luminosity, smoke etc.). All images of the dataset are characterised according to the principal colour of the fire, the luminosity, and the presence of smoke in the fire area. With this characterisation, it has been possible to determine on which kind of images each algorithm is efficient. Also a new probabilistic fire segmentation algorithm is introduced and compared to the other techniques. Benchmarking is performed in order to assess performances of 12 algorithms that can be used for the segmentation of wildland fire images.

In this letter, we propose a simple yet effective unsupervised change detection approach for multitemporal synthetic aperture radar images from the perspective of clustering. This approach jointly exploits the robust Gabor wavelet representation and the advanced cascade clustering. First, a log-ratio image is generated from the multitemporal images. Then, to integrate contextual information in the feature extraction process, Gabor wavelets are employed to yield the representation of the log-ratio image at multiple scales and orientations, whose maximum magnitude over all orientations in each scale is concatenated to form the Gabor feature vector. Next, a cascade clustering algorithm is designed in this discriminative feature space by successively combining the first-level fuzzy c-means clustering with the second-level nearest neighbor rule. Finally, the two-level combination of the changed and unchanged results generates the final change map. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

There has been quite a lot of research done in the field of Facial Expression Recognition, yet there has not been so much development in Facial Action Coding System Action Unit intensity detection. In Automated Facial Expression Recognition, intensity recognition of the Facial Action Coding System Action Units is a crucial part for it would give much broad information about the facial expression of an individual. In this research, a computationally efficient yet effective logistic regression based method that operates on a novel feature vector extracted from geometric relations between facial feature points is presented. Said method uses angles between facial feature points which are rotation invariant. The method was trained and tested on DISFA database and gave state of the art results.

This paper proposes a novel algorithm, which enhances the contrast of an input image using spatial information of pixels. The algorithm introduces a new method to compute the spatial entropy of pixels using spatial distribution of pixel gray levels. Different than the conventional methods, this algorithm considers the distribution of spatial locations of gray levels of an image instead of gray-level distribution or joint statistics computed from the gray levels of an image. For each gray level, the corresponding spatial distribution is computed using a histogram of spatial locations of all pixels with the same gray level. Entropy measures are calculated from the spatial distributions of gray levels of an image to create a distribution function, which is further mapped to a uniform distribution function to achieve the final contrast enhancement. The method achieves contrast improvement in the case of low-contrast images; however, it does not alter the image if the image’s contrast is high enough. Thus, it always produces visually pleasing results without distortions. Furthermore, this method is combined with transform domain coefficient weighting to achieve both local and global contrast enhancement at the same time. The level of the local contrast enhancement can be controlled. Several experiments on effects of contrast enhancement are performed. Experimental results show that the proposed algorithms produce better or comparable enhanced images than several state-of-the-art algorithms.

This paper presents an improved algorithm of unsupervised change detection technique by taking the same low-order fractional Fourier transform (FRFT) on multitemporal images acquired on the same geographical area but at different time instances, then generates the difference image by the absolute log-ratio operator. In order to acquire the eigenvector space, we perform principal component analysis (PCA) on m × m nonoverlapping difference image blocks. The feature vectors are extracted using m × m data blocks projection onto eigenvector space. The change detection map is generated by clustering the feature vectors using k-means algorithm into two disjoint classes: changed and unchanged. The final results obtained by the improved algorithm exhibited lower error than its preexistence.

A video contrast enhancement algorithm which automatically enhances the contrast of a video using spatial and temporal information is proposed. The algorithm is based on the observation that the contrast in a video frame can be improved by increasing the grey‐level differences between each pixel of the video frame and its neighbouring pixels. Furthermore, such an improvement should be smooth in between consecutive video frames so that continuum of contrast improvement is achieved. A two‐dimensional (2D) histogram of a video frame is constructed using mutual relationship between each pixel and its neighbouring pixels. For each video frame, a 2D target histogram is computed by considering 2D histogram of the video frame, 2D uniformly distributed histogram, and the 2D histograms of forward and backward neighbouring video frames. The contrast enhancement of the video frame is achieved by mapping the diagonal elements of the 2D input histogram to the diagonal elements of the 2D target histogram. The proposed algorithm is easy to implement and is thus suitable for real‐time contrast enhancement applications.

In a recent paper, Krinidis and Chatzis proposed a variation of fuzzy c-means algorithm for image clustering. The local spatial and gray-level information are incorporated in a fuzzy way through an energy function. The local minimizers of the designed energy function to obtain the fuzzy membership of each pixel and cluster centers are proposed. In this paper, it is shown that the local minimizers of Krinidis and Chatzis to obtain the fuzzy membership and the cluster centers in an iterative manner are not exclusively solutions for true local minimizers of their designed energy function. Thus, the local minimizers of Krinidis and Chatzis do not converge to the correct local minima of the designed energy function not because of tackling to the local minima, but because of the design of energy function.

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.

In this paper, an unsupervised change detection method for satellite images is proposed. Owing to its robustness against noise, the undecimated discrete wavelet transform is exploited to obtain a multiresolution representation of the difference image, which is obtained from two satellite images acquired from the same geographical area but at different time instances. A region-based active contour model is then applied to the multiresolution representation of the difference image for segmenting the difference image into the “changed” and “unchanged” regions. The proposed change detection method has been conducted on two types of image data sets, i.e., the synthetic aperture radar images and the optical images. The change detection results are compared with several state-of-the-art techniques. The extensive simulation results clearly show that the proposed change detection method consistently yields superior performance.

This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms.

In this paper, we propose a novel method for unsupervised change detection in satellite images. A feature vector for each pixel is extracted using the multiresolution representation of the difference image which is computed from the multitemporal satellite images of the same scene acquired at different time instances. A metric for automatically estimating the number of resolution levels used in multiresolution analysis is proposed. The dimensionality of each feature vector is reduced using principal component analysis (PCA). The feature vectors are then classified into “changed” and “unchanged” classes using k-means clustering with k = 2 to achieve a change detection map. Results are shown on real data and comparisons with the state-of-the-art techniques on advanced synthetic aperture radar (ASAR) images are provided.

In this letter, we propose a novel method for unsupervised change detection in multitemporal satellite images by minimizing a cost function using a genetic algorithm (GA). The difference image computed from the multitemporal satellite images is partitioned into two distinct regions, namely, ¿changed¿ and ¿unchanged,¿ according to the binary change detection mask realization from the GA. For each region, the mean square error (MSE) between its difference image values and the average of its difference image values is calculated. The weighted sum of the MSE of the changed and unchanged regions is used as a cost value for the corresponding change detection mask realization. The GA is employed to find the final change detection mask with the minimum cost by evolving the initial realization of the binary change detection mask through generations. The proposed method is able to produce the change detection result on the difference image withouta prioriassumptions. Change detection results are shown on multitemporal Advanced Synthetic Aperture Radar images acquired by the ESA/Envisat satellite and on multitemporal optical images acquired by the Landsat multispectral scanner. The comparisons with the state-of-the-art change detection methods are provided.

In this letter, a complex wavelet-domain image resolution enhancement algorithm based on the estimation of wavelet coefficients is proposed. The method uses a forward and inverse dual-tree complex wavelet transform (DT-CWT) to construct a high-resolution (HR) image from the given low-resolution (LR) image. The HR image is reconstructed from the LR image, together with a set of wavelet coefficients, using the inverse DT-CWT. The set of wavelet coefficients is estimated from the DT-CWT decomposition of the rough estimation of the HR image. Results are presented and discussed on very HR QuickBird data, through comparisons between state-of-the-art resolution enhancement methods.

In this paper, an unsupervised change-detection method for multitemporal satellite images is proposed. The algorithm exploits the inherent multiscale structure of the dual-tree complex wavelet transform (DT-CWT) to individually decompose each input image into one low-pass subband and six directional high-pass subbands at each scale. To avoid illumination variation issue possibly incurred in the low-pass subband, only the DT-CWT coefficient difference resulted from the six high-pass subbands of the two satellite images under comparison is analyzed in order to decide whether each subband pixel intensity has incurred a change. Such a binary decision is based on an unsupervised thresholding derived from a mixture statistical model, with a goal of minimizing the total error probability of change detection. The binary change-detection mask is thus formed for each subband, and all the produced subband masks are merged by using both the intrascale fusion and the interscale fusion to yield the final change-detection mask. For conducting the performance evaluation of change detection, the proposed DT-CWT-based unsupervised change-detection method is exploited for both the noise-free and the noisy images. Extensive simulation results clearly show that the proposed algorithm not only consistently provides more accurate detection of small changes but also demonstrates attractive robustness against noise interference under various noise types and noise levels.

In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and k-means clustering. The difference image is partitioned into h times h nonoverlapping blocks. S, S les h2, orthonormal eigenvectors are extracted through PCA of h times h nonoverlapping block set to create an eigenvector space. Each pixel in the difference image is represented with an S-dimensional feature vector which is the projection of h times h difference image data onto the generated eigenvector space. The change detection is achieved by partitioning the feature vector space into two clusters using k-means clustering with k = 2 and then assigning each pixel to the one of the two clusters by using the minimum Euclidean distance between the pixel's feature vector and mean feature vector of clusters. Experimental results confirm the effectiveness of the proposed approach.

In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images. The difference image which is computed from multitemporal images acquired on the same geographical area at two different time instances is decomposed usingS-levels undecimated discrete wavelet transform (UDWT). For each pixel in the difference image, a multiscale feature vector is extracted using the subbands of the UDWT decomposition and the difference image itself. The final change detection map is achieved by clustering the multiscale feature vectors usingk-means algorithm into two disjoint classes: changed and unchanged. Experimental results confirm the efficacy of the proposed approach on both optical and synthetic aperture radar images.

In this paper, fuzzy logic enhanced generic color model for fire pixel classification is proposed. The model uses YCbCr color space to separate the luminance from the chrominance more effectively than color spaces such as RGB or rgb. Concepts from fuzzy logic are used to replace existing heuristic rules and make the classification more robust in effectively discriminating fire and fire like colored objects. Further discrimination between fire and non fire pixels are achieved by a statistically derived chrominance model which is expressed as a region in the chrominance plane. The performance of the model is tested on two large sets of images; one set contains fire while the other set contains no fire but has regions similar to fire color. The model achieves up to 99.00% correct fire detection rate with a 9.50% false alarm rate.

In this paper, we propose a real-time fire-detector which combines foreground information with statistical color information to detect fires. The foreground information which is obtained using adaptive background information is verified by the statistical color information to determine whether the detected foreground object is a candidate for fire or not. The output of the both stages is analyzed in consecutive frames which is the verification process of fire that uses the fact that fire never stays stable in visual appearance. The frame processing rate of the detector is about 30 fps with image size of 176times144 which enables the proposed detector to be applied for real-time applications

Facial feature extraction is a fundamental problem in image processing. Correct extraction of features is essential for the success of many applications. Typical feature extraction algorithms fail for low resolution images which do not contain sufficient facial detail. A region-based super-resolution aided facial feature extraction method for low resolution video sequences is described. The region based approach makes use of segmented faces as the region of interest whereby a significant reduction in computational burden of the super-resolution algorithm is achieved. The results indicate that the region-based super-resolution aided extraction algorithm provides significant performance improvement in terms of correct detection in accurately locating the facial feature points.