Recent advances in single-and multi-image superresolution have revealed the limitations of classical image similarity metrics (like peak signal-to-noise ratio), as they often fail to align with human perception when evaluating the visual quality of super-resolved outputs.In this paper, we explore how to exploit keypoint-based metrics to evaluate super-resolution image quality.Specifically, we explore two correlated metrics: (i) a multiscale index proposal measure capturing salience of keypoints, and (ii) a repeatability metric quantifying how consistently the corresponding keypoints are identified in super-resolved and ground-truth images.Experiments on several simulated and real-world datasets show that the repeatability correlates with subjective judgments, and multi-scale index proposal can be helpful for difficult datasets when other metrics are insufficient.

Super-resolution reconstruction is aimed at generating images of high spatial resolution from low-resolution observations. State-of-the-art super-resolution techniques underpinnedwith deep learning allow for obtaining results of outstanding visual quality, but it is seldom verified whether they constitute a valuable source for specific computer vision applications.In this paper, we investigate the possibility of employing superresolution as a preprocessing step to improve optical character recognition from document scans.To achieve that, we propose to train deep networks for single-image super-resolution in a task-driven way to make them better adapted for the purpose of text detection.As problems limited to a specific task are heavily ill-posed, we introduce a multi-task loss function that embraces components related with text detection coupled with those guided by image similarity.The obtained results reported in this paper are encouraging and they constitute an important step towards real-world super-resolution of document images.

Insufficient spatial resolution remains a serious obstacle for many potential applications of Sentinel-2 images. Therefore, a number of super-resolution techniques have been proposed, including single-image and multi-image approaches. While the former may hallucinate (rather than reconstruct) the image details, the latter are underpinned with information fusion that helps address these issues. However, given the five-days-long revisit time, a multi-temporal series of Sentinel-2 images contains temporal changes that affect the performance of multi-image super-resolution. In this paper, we propose a solution that allows for selecting a single image in the series to specify the point in time, at which the scene should be reconstructed. We report the results of our extensive experiments which confirm that our approach leads to improving the reconstruction quality, while preserving the temporal consistency. Importantly, the proposed solutions are potentially applicable to a variety of existing super-resolution techniques and they are not limited to Sentinel-2 images.

Super-resolution is aimed at reconstructing high-resolution images from low-resolution observations. State-of-the-art approaches underpinned with deep learning allow for obtaining outstanding results, generating images of high perceptual quality. However, it often remains unclear whether the reconstructed details are close to the actual ground-truth information and whether they constitute a more valuable source for image analysis algorithms. In the reported work, we address the latter problem, and we present our efforts toward learning super-resolution algorithms in a task-driven way to make them suitable for generating high-resolution images that can be exploited for automated image analysis. In the reported initial research, we propose a methodological approach for assessing the existing models that perform computer vision tasks in terms of whether they can be used for evaluating super-resolution reconstruction algorithms, as well as training them in a task-driven way. We support our analysis with experimental study and we expect it to establish a solid foundation for selecting appropriate computer vision tasks that will advance the capabilities of real-world super-resolution.

Abstract Designing routing schedules is a pivotal aspect of smart delivery systems. Therefore, the field has been blooming for decades, and numerous algorithms for this task have been proposed for various formulations of rich vehicle routing problems. There is, however, an important gap in the state of the art that concerns the lack of an established and widely-adopted approach toward thorough verification and validation of such algorithms in practical scenarios. We tackle this issue and propose a comprehensive validation approach that can shed more light on functional and non-functional abilities of the solvers. Additionally, we propose novel similarity metrics to measure the distance between the routing schedules that can be used in verifying the convergence abilities of randomized techniques. To reflect practical aspects of intelligent transportation systems, we introduce an algorithm for elaborating solvable benchmark instances for any vehicle routing formulation, alongside the set of quality metrics that help quantify the real-life characteristics of the delivery systems, such as their profitability. The experiments prove the flexibility of our approach through utilizing it to the NP-hard pickup and delivery problem with time windows, and present the qualitative, quantitative, and statistical analysis scenarios which help understand the capabilities of the investigated techniques. We believe that our efforts will be a step toward the more critical and consistent evaluation of emerging vehicle routing (and other) solvers, and will allow the community to easier confront them, thus ultimately focus on the most promising research avenues that are determined in the quantifiable and traceable manner.

Super-resolution (SR), i.e. reconstruction of high-resolution (HR) images from low-resolution (LR) samples, plays a vital role in remote sensing. Multi-image SR (MISR) methods leverage information fusion from a series of LR images acquired over time to reconstruct HR images. Deep neural networks form the foundation of state-of-the-art MISR techniques, and selecting an appropriate metric for training them is crucial to obtain reliable results. In this paper, we propose a novel approach for training MISR networks by incorporating the learned perceptual image patch similarity (LPIPS) metric as the loss function. Traditionally, SR methods rely on metrics based on pixel-wise similarity, which may not be optimal when LR and HR images differ significantly in histogram characteristics. By utilizing the LPIPS metric, which is consistent with human perception, the training process is directed towards better reconstruction of image details rather than strict pixel-wise compliance. Experimental results on the MuS2 dataset, consisting of LR images from Sentinel-2 and HR images from WorldView-2, demonstrate the effectiveness of our approach.

Rock segmentation in lunar images is a crucial computer vision task for visual navigation of planetary rovers. Even though numerous approaches have been already proposed to address this task, many solutions are underpinned with computationally-intensive deep learning models, which makes them unsuitable for on-board processing. In the study reported here, we address this important problem and we propose a sequential pipeline, which combines a U-Net-based network for rock segmentation with a YOLO model for final object detection. We demonstrate that putting two lightweight models together improves the detection performance, making it close to that obtained with full-sized architectures. Even though this is an initial study, the obtained results indicate that this direction is promising and it is worthy of further investigation.

Super-resolution is aimed at enhancing image spatial resolution and it has been intensively explored for many years. The recent advancements, underpinned with deep learning, also include techniques developed specifically for hyper-spectral data. However, most of the emerging methods are validated in application-independent scenarios, which often rely on an unrealistic experimental setup—the reconstruction is performed from simulated low-resolution images (degraded from an original image) with the goal of inverting the degradation process and restoring the original image. This leads to over-optimistic assessment of super-resolution capabilities and limits their practical applications. In this paper, we demonstrate task-based validation for different types of hyperspectral PRISMA image super-resolution, including pan-sharpening, fusion of multispectral and hyperspectral data, as well as single-image super-resolution. The obtained results reported in the paper are encouraging and they help better understand the value of super-resolved PRISMA images.

Hyperspectral unmixing is one of the most challenging tasks in the analysis of such data. There have been an array of algorithms proposed for this problem so far, but they are virtually always verified using random sampling, where training and test examples are drawn from the same image. Since such samples are spatially correlated and may be positioned close to each other, random sampling can induce the training-test information leak in the techniques that exploit spatial information during the unmixing process. We want to raise the attention of the community about this validation flaw in the context hyperspectral unmixing. We introduce the algorithm for unbiased validation of the unmixing techniques through splitting hyperspectral images into training and test samples that do not suffer from the training-test information leak. The experiments showed that the widely-used random sampling verification strategy leads to overly optimistic conclusions concerning the algorithm’s performance. This problem was mitigated with the proposed approach which allows us to rigorously validate unmixing techniques.

Moon exploration has gained significant momentum in recent decades, with growing interest from space agencies and private investors. A diverse range of activities is associated with moon exploration, encompassing spacecraft design, payload transportation, launcher capabilities, resource identification and mining, and establishing a sustained presence on our only natural satellite. Such endeavors would require the shipment of both scientific and life-sustaining equipment. However, communication between Earth’s mission control and the Moon’s bases can still present challenges. In this paper, we introduce Cognition – a rover-lander distributed system that approaches this problem by distributing the data processing between the rover and the lander. The primary goal of the Cognition system is to optimize lunar surface exploration by minimizing data transmission to the Earth’s surface, prioritizing the transfer of valuable data, and augmenting the level of autonomy in the process.

In this paper, we present our study focused on applying a vision transformer-based pansharpening technique to enhance PRISMA satellite hyperspectral data. The PRISMA mission, launched by the Italian Space Agency, captures hyperspectral images comprising visible and near infra-red, as well as short-wave infra-red channels. By integrating the panchromatic image of high spatial resolution with the hyperspectral data of high spectral resolution, the pansharpening process consists in producing spatially-enhanced hyperspectral imagery. Our research involves modifying and adapting the state-of-the-art HyperTransformer architecture to effectively process real-life PRISMA data. The evaluation of our model’s performance utilizes PRISMA L2D data, encompassing simulated low-resolution data and real-life data. We employ quantitative metrics and visual examination to assess the results. We also highlight the importance of choosing right PRISMA data processing level for the pansharpening process. The proposed pansharpening model successfully enhances PRISMA data for practical applications, contributing to the advancement of Earth observation techniques.

Multispectral Sentinel-2 images are a valuable source of Earth observation data, however spatial resolution of their spectral bands limited to 10 m, 20 m, and 60 m ground sampling distance remains insufficient in many cases. This problem can be addressed with super-resolution, aimed at reconstructing a high-resolution image from a low-resolution observation. For Sentinel-2, spectral information fusion allows for enhancing the 20 m and 60 m bands to the 10 m resolution. Also, there were attempts to combine multitemporal stacks of individual Sentinel-2 bands, however these two approaches have not been combined so far. In this paper, we introduce DeepSent—a new deep network for super-resolving multitemporal series of multispectral Sentinel-2 images. It is underpinned with information fusion performed simultaneously in the spectral and temporal dimensions to generate an enlarged multispectral image. In our extensive experimental study, we demonstrate that our solution outperforms other state-of-the-art techniques that realize either multitemporal or multispectral data fusion. Furthermore, we show that the advantage of DeepSent results from how these two fusion types are combined in a single architecture, which is superior to performing such fusion in a sequential manner. Importantly, we have applied our method to super-resolve real-world Sentinel-2 images, enhancing the spatial resolution of all the spectral bands to 3.3 m nominal ground sampling distance, and we compare the outcome with very high-resolution WorldView-2 images. We will publish our implementation upon paper acceptance, and we expect it will increase the possibilities of exploiting super-resolved Sentinel-2 images in real-life applications.

Improving agricultural practices through exploiting the recent imaging and machine learning advancements plays a key role nowadays to ensure sustainable food security, and to help us deal with the climate change. Quantifying soil parameters can lead to optimizing the fertilization process but it is cumbersome, time-consuming and difficult to scale, as it requires performing in-situ soil measurements that are later analyzed in the laboratory settings. In the HYPER-VIEW challenge, we aim at automating the soil analysis thanks to the utilization of hyperspectral images that capture very detailed information about the scanned objects in hundreds of contiguous hyperspectral bands. Such imagery can be effectively analyzed using an array of classical and deep machine learning approaches. Also, the AI techniques can be deployed on-board the imaging satellites— it opens new doors related to the scalability of the solution. The winners of the challenge will be offered a unique opportunity to run their proposed solution in orbit, on-board the Intuition-1 satellite, equipped with a hyperspectral imager and on-board AI capabilities.

Cloud detection is one of the most important image pre-processing steps that can be performed on-board satellites. It may allow us to reduce the amount of data to analyze or downlink by pruning the cloudy areas, or to make the satellites more autonomous through data-driven image acquisition re-scheduling of the areas obscured by clouds. Thus, building the cloud detection algorithms that can be ultimately deployed in orbit became an important research avenue. In this paper, we investigate the robustness of the fully-convolutional neural networks for cloud detection against the atmospheric conditions that resemble real acquisition settings of the Intuition-1 mission. Our experiments, performed over the original and simulated Landsat-8 images, with the latter reflecting target conditions, shed more light on the performance of deep models and showed how can we verify their robustness in Earth observation tasks for which real images do not exist yet.

Multi-image super-resolution is a branch of super-resolution reconstruction techniques aiming to resolve a set of lowresolution images into a high-resolution one. Unlike singleimage super-resolution, its goal is to fuse information embedded in different images depicting the same scene, usually captured at different times. Such data combination contains more high-resolution information than a single image, thus allowing for more accurate reconstruction results. One of the most critical challenges is to prepare training data for multi-image super-resolution since only a few datasets are available here, especially for satellite imaging applications. For this reason, many studies are conducted using simulated low-resolution images, but the results obtained for real-life data are often unsatisfactory. To overcome this problem, we propose a new semi-simulated approach of creating lowresolution images for training that resemble real-life ones much more accurately. We also investigate the performance of selected deep learning models trained with simulated and semi-simulated datasets and we show that the latter achieve better results when applied to real-world images.

The recent advances in Earth observation and artificial in-telligence allow us to improve the agricultural management practices through effectively exploiting the spectral, spatial, and temporal characteristics of the area of interest captured by satellite images. In this paper, we tackle the problem of extracting high-resolution (2.5-meter) cultivated land maps from Sentinel-2 multispectral images, and propose a machine learning algorithm for this task. It aggregates the spectral, spatial, and temporal features of the upsampled images, and is independent from the number of observations captured for a given scene. The experimental results, performed within the framework of the Enhanced Sentinel-2 Agriculture chal-lenge show that our technique manifests high generalization abilities over the unseen data and elaborates high-quality cul-tivated land maps. Finally, utilizing this algorithm led us to taking the $6^{\text{th}}$ place in the aforementioned challenge.

Super-resolution reconstruction consists in generating a high-resolution image from a single low-resolution image or multiple images presenting the same area of interest. Existing state-of-the-art approaches to single-image and multi-image super-resolution are based on deep learning that requires large amounts of training data. They are commonly obtained by simulating low-resolution images from an original image treated as a high-resolution reference, but such simulation may not reflect the real-life operating conditions. Therefore, a serious obstacle in deploying super-resolution in real-world cases results from the lack of training data that would encompass real low-resolution images coupled with a real high-resolution reference. In this paper, we propose a new data augmentation technique underpinned with learning the relation between high and low resolution. This helps reduce the requirements concerned with the amount of real-life data necessary to train a super-resolution network, while providing higher-quality data for training, compared with the simulated low-resolution images. Our initial experimental results reported in the paper confirm that the proposed approach is suitable for multi-image super-resolution.

Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multi- and hyperspectral imagery which captures more detailed spectral information concerning the scanned area, hence allows us to benefit from subtle spectral features during the analysis process in agricultural applications. We introduce an approach for extracting 2.5m cultivated land maps from 10m Sentinel-2 multispectral image series which benefits from a compact graph convolutional neural network. The experiments indicate that our models not only outperform classical and deep machine learning techniques through delivering higher-quality segmentation maps, but also dramatically reduce the memory footprint when compared to U-Nets (almost 8k trainable parameters of our models, with up to 31M parameters of U-Nets). Such memory frugality is pivotal in the missions which allow us to uplink a model to the AI-powered satellite once it is in orbit, as sending large nets is impossible due to the time constraints.

Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic unmixing techniques, and can be effectively deployed onboard Earth observation satellites equipped with hyperspectral imagers. In this letter, we follow this research pathway and propose a multi-branch convolutional neural network that benefits from fusing spectral, spatial, and spectral-spatial features in the unmixing process. The results of our experiments, backed up with the ablation study, revealed that our techniques outperform others from the literature and lead to higher-quality fractional abundance estimation. Also, we investigated the influence of reducing the training sets on the capabilities of all algorithms and their robustness against noise, as capturing large and representative ground-truth sets is time-consuming and costly in practice, especially in emerging Earth observation scenarios.

Super-resolution consists in reconstructing a high-resolution image from single or multiple low-resolution observations. Deep learning has been reported extremely successful for single-image super-resolution, but its applications to the multiple-image scenarios are limited due to the challenges that arise from feeding a network with a stack of images with sub-pixel translations. In this paper, we introduce Magnet—a new graph neural network that benefits from representing the input low-resolution images as a graph. This enables us to exploit the sub-pixel shifts among the input images while preserving the original low-resolution pixel values for feature extraction and information fusion. Despite a relatively simple architecture, Magnet outperforms the state-of-the-art methods for multiple-image super-resolution, and due to the flexible graph representation, it allows for using a variable number of low-resolution images for reconstruction.

Cloud detection is an important pre-processing step that allows us to significantly reduce the amount of satellite imagery which should undergo further processing. In this paper, we investigate the impact of training set selection on the abilities of fully-convolutional neural networks for this task. Our experiments, performed over a range of Landsat-8 satellite images, show that the performance of deep models can substantially vary for different training samples, especially in the case of challenging scenes, such as those capturing snowy areas.

Super-resolution (SR) reconstruction is a common term for techniques aimed at generating a high-resolution image from a single low-resolution image or multiple images showing the same scene. Multiple-image SR benefits from data fusion which allows for more accurate reconstruction of the underlying high-resolution information. Deep learning is extensively used for single-image SR, but its application to multiple-image SR is much less explored. Recently, several deep networks were proposed to enhance Proba-V images, and in this paper, we focus on employing them to super-resolve the Sentinel-2 images. In particular, we investigate the influence of the training data, including real and simulated low-resolution images, on the final SR outcome. Also, we make the simulated data publicly available.

Although deep learning is gaining more widespread use in hyperspectral image analysis, it is challenging to train high-capacity models in a supervised way—ground-truth sets are expensive to obtain, and they are practically always extremely imbalanced. To deal with the problem of missing ground-truth data, its high dimensionality and potential redundancy, we introduce a novel unsupervised feature learning technique to extract discriminative features from the original data. It exploits recurrent neural network-based asymmetric autoencoders (AEs) to learn the compressed representation of unlabeled data, and can elaborate both spectral and spectral–spatial features. Our extractors can be incorporated into the unsupervised segmentation pipeline—they can be followed by any clustering algorithm. The experiments revealed that our approaches deliver high-quality segmentation without any prior class labels, and are one order of magnitude faster than 3-D convolutional AEs. Our algorithms outperform or work on par with other approaches while allowing for significant data reduction.

The use of deep-learned features has recently allowed for improving the performance of skin detection and segmentation. In particular, fully-convolutional U-Nets, proposed for segmenting medical images, occurred to be extremely effective here. However, the spatial context, which is rather narrow for U-Nets, may be more important for skin segmentation than for segmenting other image structures. We propose Skinny-a lightweight U-Net-based architecture that extends the range of multi-scale analysis. The results of our experiments indicate that Skinny outperforms the state-of-the-art skin segmentation techniques, rendering the F-score of 92.3% and 94.9% for the ECU and HGR datasets, respectively.

Support vector machines (SVMs) are a supervised learning technique that can be applied in both binary and multi-class classification and regression tasks. SVMs seamlessly handle continuous and categorical variables. Their training is, however, both time- and memory-costly for large training data, and selecting an incorrect kernel function or its hyperparameters leads to suboptimal decision hyperplanes. In this paper, we introduce a memetic algorithm for evolving SVM training sets with adaptive radial basis function kernels to not only make the deployment of SVMs easier for emerging big data applications, but also to improve their generalization abilities over the unseen data. We build upon two observations: first, only a small subset of all training vectors, called the support vectors, contribute to the position of the decision boundary, hence the other vectors can be removed from the training set without deteriorating the performance of the model. Second, selecting different kernel hyperparameters for different training vectors may help better reflect the subtle characteristics of the space while determining the hyperplane. The experiments over almost 100 benchmark and synthetic sets showed that our algorithm delivers models outperforming both SVMs optimized using state-of-the-art evolutionary techniques, and other supervised learners.

Super-resolution reconstruction is a process aimed at enhancing image spatial resolution. To evaluate the quality of super-resolution, the reconstruction outcome is compared with a ground-truth reference image, and the dissimilarity between them is commonly treated as a determinant of the reconstruction quality. While this is straightforward for simulated data, it becomes more challenging for real-world scenarios, in which reference images and the reconstruction inputs are acquired using different imaging sensors. In such cases, the dissimilarity also results from other factors concerned with different sensor characteristics. In a recently organized Proba-V Super Resolution Challenge, the reconstruction quality was assessed using a modified peak signal-to-noise ratio which compensates for small shifts and global changes in the brightness. In the study reported here, we investigate a number of image similarity metrics to verify their robustness against different levels of distortions applied to Proba-V images. We expect that the reported results will help in choosing appropriate metrics while developing new super-resolution solutions aimed at real-world scenarios.

Hyperspectral images provide detailed information about the scanned objects, as they capture their spectral characteristics within a large number of wavelength bands. Classification of such data has become an active research topic due to its wide applicability. In this paper, we introduce a new spectral-spatial convolutional neural network, benefitting from a battery of data augmentation techniques which help deal with a real-life problem of lacking ground-truth training data. Our experiments showed that the proposed method works in real time and outperforms other spectral-spatial algorithms.

Super-resolution (SR) reconstruction is a process aimed at enhancing the spatial resolution of images, either from a single observation, based on the learned relation between low and high resolution, or from multiple images presenting the same scene. SR is particularly important, if it is not feasible to acquire images at the desired resolution, while there are single or many observations available at lower resolution - this is inherent to a variety of remote sensing scenarios. Recently, we have witnessed substantial improvement in single-image SR attributed to the use of deep neural networks for learning the relation between low and high resolution. Importantly, deep learning has not been widely exploited for multiple-image super-resolution, which benefits from information fusion and in general allows for achieving higher reconstruction accuracy. In this letter, we introduce a new approach to combine the advantages of multiple-image fusion with learning the low-to-high resolution mapping using deep networks. The results of our extensive experiments indicate that the proposed framework outperforms the state-of-the-art SR methods.

Data augmentation helps improve generalization capabilities of deep neural networks when only limited ground-truth training data are available. In this letter, we propose test-time augmentation of hyperspectral data, which is executed during the inference rather than before the training of deep networks. We introduce two augmentation techniques, which can be applied at both training time and test time. The experiments revealed that our augmentations boost generalization of deep models and work in real time, and the test-time approach can be combined with training-time techniques to enhance the classification accuracy.

Deep learning has established the state of the art in multiple fields, including hyperspectral image analysis. However, training large-capacity learners to segment such imagery requires representative training sets. Acquiring such data is human-dependent and time-consuming, especially in earth observation scenarios, where the hyperspectral data transfer is very costly and time-constrained. In this letter, we show how to effectively deal with a limited number and size of available hyperspectral ground-truth sets and apply transfer learning for building deep feature extractors. Also, we exploit spectral dimensionality reduction to make our technique applicable over hyperspectral data acquired using different sensors, which may capture different numbers of hyperspectral bands. The experiments, performed over several benchmarks and backed up with statistical tests, indicated that our approach allows us to effectively train well-generalizing deep convolutional neural nets even using significantly reduced data.

Ensemble methods train multiple classifiers and combine their outcomes to improve the generalization ability of a learning system. Although there exist methods for automatic creation of ensembles, they require heavy fine-tuning and the impact of their hyper-parameters on the optimization process very often remains unknown. In this paper, we propose a genetic algorithm for evolving classification ensembles. It is coupled with a pre-processing routine which deals with potential data imbalance and redundancy within the training and/or feature sets. Our experimental study, performed over binary and multi-class benchmark datasets and backed up with statistical analysis, revealed that the proposed technique outperforms other algorithms for building multiple classifier systems. It also helped understand the impact of various (hyper-)parameters of our method on its exploration/exploitation capabilities.

Data augmentation helps improve generalization of deep neural networks, and can be perceived as implicit regularization. It is pivotal in scenarios in which the amount of ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a common problem in medical image analysis, especially tumor delineation-in this paper, we focus on brain-tumor segmentation from magnetic resonance imaging (MRI), and propose a novel augmentation technique which exploits image registration to benefit from subtle spatial and/or tissue characteristics captured within the training set. We used a set of MRI scans of 44 low-grade glioma patients, augmented it using the proposed technique, and exploited it to train U-Net-based deep networks. The results show that our augmentation delivers statistically important boost of performance without sacrificing inference speed.

The capabilities of super-resolution (SR) reconstruction (i.e., techniques for enhancing image spatial resolution) have been boosted recently by the use of deep convolutional neural networks. For SR, they are learned using huge training sets composed of original images, each of which is coupled with a low-resolution counterpart. In this paper, we explore how the SR performance depends on the procedure employed to obtain the training data. Up to date, this has not been given much attention-commonly, bicubic downsampling is used. Our extensive experimental study indicates that the training data characteristics have a large impact on the reconstruction accuracy, and the widely-adopted approach is not the most effective for dealing with satellite images. Overall, we argue that developing better training data preparation routines may be pivotal in making SR suitable for real-world applications.

Hyperspectral satellite imaging has been gaining enormous research attention due to the latest advancements in the sensor technology, and the amount of information it conveys. However, its efficient analysis, transfer, and storage are still big practical issues which need to be endured in on-board applications. In this paper, we verify if the simulated multispectral data (with significantly smaller number of bands) can be segmented with accuracy as high as obtained over its corresponding original hyperspectral imagery. Our experimental study, backed up with statistical tests, revealed that it is possible to dramatically decrease the transfer and storage requirements of the original hyperspectral data by simulating its multispectral counterpart without adversely affecting the classification performance of popular supervised learners.

Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, and hence, automated approaches for precise segmentation of such imagery are being rapidly developed. In this letter, we share our observations on the strategy for validating hyperspectral image segmentation algorithms currently followed in the literature, and show that it can lead to overoptimistic experimental insights. We introduce a new routine for generating segmentation benchmarks and use it to elaborate ready-to-use hyperspectral training-test data partitions. They can be utilized for fair validation of new and existing algorithms without any training-test data leakage.

Deep neural networks (DNNs) have achieved unprecedented success in a wide array of tasks. However, the performance of these systems depends directly on their hyper-parameters which often must be selected by an expert. Optimizing the hyper-parameters remains a substantial obstacle in designing DNNs in practice. In this work, we propose to select them using particle swarm optimization (PSO). Such biologically-inspired approaches have not been extensively exploited for this task. We demonstrate that PSO efficiently explores the solution space, allowing DNNs of a minimal topology to obtain competitive classification performance over the MNIST dataset. We showed that very small DNNs optimized by PSO retrieve promising classification accuracy for CIFAR-10. Also, PSO improves the performance of existing architectures. Extensive experimental study, backed-up with the statistical tests, revealed that PSO is an effective technique for automating hyper-parameter selection and efficiently exploits computational resources.

It has been reported in many works on skin detection and segmentation from color images that skin color models suffer from low specificity and high variance of the skin color, and this problem can be addressed by conforming the skin model to a presented scene. Here, we introduce a new hybrid adapta tion system which combines two strategies, namely (i) adaptation from a detected facial region and (ii) a self-adaptive scheme that creates a local model based on the response obtained using the global one. As a result of this hybrid adaptation, we obtain a local skin color model and we use it to extract seeds for the geodesic distance transform that determines the boundaries of skin regions. The results of our extensive experimental study confirm that the proposed algorithm outperforms several state-of-the-art methods, as well as our earlier adaptive skin detectors.

This paper introduces a new method for detecting and localizing hand landmarks in 2D color images. Location of the hand landmarks is an important source of information for recognizing hand gestures, effectively exploited in a number of recent methods which operate from the depth maps. However, this problem has not yet been satisfactorily solved for 2D color images. Here, we propose to analyze the skin-presence masks, as well as the directional image of a hand using the distance transform and template matching. This makes it possible to detect the landmarks located both at the contour and inside the hand masks. Moreover, we performed an extensive experimental study to compare the proposed method with a number of state-of-the-art algorithms. The obtained quantitative and qualitative results clearly indicate that our approach outperforms other methods, which may help improve the existing gesture recognition systems.

In this paper, we present Adaptive Vision Studio (AVS) - a novel tool for creating image processing and analysis algorithms. AVS has been applied in post-graduate computer vision course for students of Automatic Control and Biotechnology at Silesian University of Technology. This software is a powerful environment with ready-for-use image analysis filters for computer vision experts as well as for engineers, who are beginners in this field. AVS has been published as a freeware version for noncommercial and educational purposes recommended for students and engineers, who want to learn how to develop complex image processing algorithms. Lite version of AVS is freely available at https://adaptive-vision.com.

Solving the job shop scheduling problem (JSP) is a vital research topic due to its wide practical applicability. It is an NP-hard discrete optimization problem which was transformed into a plethora of variants reflecting other real-life scenarios. In this paper, we propose an adaptive memetic algorithm (MA) to solve the JSP. It consists in determining a schedule for completing jobs (divided into operations) on a set of available machines. MAs, which are the hybrids of genetic algorithms and refinement procedures, were shown to be very efficient in tackling complex problems in many fields of science and engineering. In the proposed algorithm (AMXMA), a number of children are generated for each pair of parents to exploit them intensively. We keep three solution representations (if necessary) within each individual to avoid the necessity of transforming one representation into another required by various crossover operators. In addition, we introduce the adaptive selection scheme which is dynamically controlled on the fly to effectively balance the exploration and exploitation of the search space. An extensive experimental study performed on a widely-used benchmark set of problems with various sizes shows that AMXMA is extremely efficient in terms of the computation time and allows for fast convergence to very high-quality solutions. We show that AMXMA is highly competitive compared with other state-of-the-art algorithms.

In this paper we propose a new memetic algorithm (MASVM) for fast and efficient selection of a valuable training set for support vector machines (SVMs). This is a crucial step especially in case of large and noisy data sets, since the SVM training has high time and memory complexity. The majority of state-of-the-art methods exploit the data geometry analysis, both in the input and kernel space. Although evolutionary algorithms have been proven to be very efficient for this purpose, they have not been extensively studied so far. Here, we propose a new method employing an adaptive genetic algorithm enhanced by some refinement techniques. The refinements are based on utilizing a pool of the support vectors identified so far at various steps of the algorithm. Extensive experimental study performed on the well-known benchmark, real-world and artificial data sets clearly confirms the efficacy, robustness and convergence capabilities of the proposed approach, and shows that it is competitive compared with other state-of-the-art techniques.

This study presents a novel multilevel algorithm for gaze direction recognition from static images. Proposed solution consists of three stages: (i) eye pupil localization using a multistage ellipse detector combined with a support vector machines verifier, (ii) eye bounding box localization calculated using a hybrid projection function and (iii) gaze direction classification using support vector machines and random forests. The proposed method has been tested on Eye-Chimera database with very promising results. Extensive tests show that eye bounding box localization allows us to achieve highly accurate results both in terms of eye location and gaze direction classification.

In this paper we propose a new method for skin detection in color images which consists in spatial analysis using the introduced texture-based discriminative skin-presence features. Color-based skin detection has been widely explored and many skin color modeling techniques were developed so far. However, efficacy of the pixel-wise classification is limited due to an overlap between the skin and non-skin pixels reported in many color spaces. To increase the discriminating power of the skin classification schemes, textural and spatial features are often exploited for skin modeling. Our contribution lies in using the proposed discriminative feature space as a domain for spatial analysis of skin pixels. Contrary to existing approaches, we extract the textural features from the skin probability maps rather than from the luminance channel. Presented experimental study confirms that the proposed method outperforms alternative skin detection techniques, which also involve analysis of textural and spatial features.

This paper introduces a new method for adaptive skin detection in color images combined with spatial analysis of skin pixels. It has been reported in many works that adaptation of a skin color model to a particular image may decrease the false positives, however the false negatives are considerably high unless a local model is combined with the global one. Another possibility for improvement is to analyze spatial properties of the pixels classified as skin, but this operation strongly depends on the seed extraction technique. Our contribution lies in using a local dynamic skin model learned from the detected faces to extract seeds for the spatial analysis. We present an extensive experimental study confirming that our method outperforms alternative skin detection techniques.

This paper introduces a new parallel algorithm (PA) for fast hand shape classification. This problem is challenging as a hand is characterized by a high number of degrees of freedom. Our objective is to design and implement a robust algorithm suitable for real-time applications. We show how the analysis time can be decreased, together with the increase of the classification accuracy, by the means of parallelization. Also, we propose to combine the shape contexts approach with the appearance-based techniques to increase the efficacy of the PA. An extensive experimental study confirms the effectiveness of the proposed PA compared with other state-of-the-art methods.

This paper presents a new method for image colorization based on manually added scribbles. We determine color propagation paths in the image by minimizing the geodesic distance from the scribbles using Dijkstra algorithm. After that, chrominance blending is performed to colorize the image. Our contribution lies in proposing the competitive approach for selecting an appropriate type of the path cost. Moreover, we introduce a modified chrominance distance calculated along each path. Benefits of these improvements are explained and justified. The experiments confirmed that the proposed algorithm yields better results compared with other well-established methods.

In this paper, we show how to extract gender discriminating features from 2.5D facial needle-maps. The standard eigenspace analysis method for non-Euclidean data is principal geodesic analysis (PGA). Based on PGA, we propose a novel supervised weighted PGA method which incorporates local weights into standard PGA to improve gender discriminating capability of the extracted features. The weight map is iteratively optimized from the labeled data, which is different from other gender relevant weights used in the literature. Experimental results illustrate the effectiveness of this method and its successful application to gender classification.