nils-herrmann/clean_scirepeval huggingface dataset:
Region-Based Classification of Multisensor Optical-SAR Images

Multispectral and synthetic aperture radar (SAR) images are known to exhibit complementary properties: unlike optical sensors, SAR provides information about the soil roughness and moisture, and acquires useful data despite clouds and Sun-illumination conditions. However, the analysis of the resulting images turns out to be more difficult, as compared to the use of optical imagery, due to the noise-like speckle phenomenon. In order to exploit this complementarity for classification purposes, a criticality relies in the definition of accurate joint optical-SAR statistical models, due to the different physical natures of these two data typologies and to the corresponding differences in the related parametric models. In this paper, a region-based semiparametric classification technique is proposed for multisensor optical-SAR images. The method combines the tree-structured Markov random field approach to segmentation with the dependence tree approach to probability density estimation and with case-specific bivariate models for the distributions of optical and SAR data. A Bayesian decision rule is formulated at the segment level in order to incorporate spatial-contextual information and to gain robustness against noise.
Detection of microcalcifications clusters in mammograms through TS-MRF segmentation and SVM-based classification

At present, mammography is the only not invasive diagnostic technique allowing the diagnosis of a breast cancer at a very early stage. A visual clue of such disease particularly significant is the presence of clusters of microcalcifications. Reliable methods for an automatic detection of such clusters are very difficult to accomplish because of the small size of the microcalcifications and of the poor quality of the digital mammograms. A method designed for this task is described. The mammograms are firstly segmented by means of the tree structured Markov random field algorithm which extracts the elementary homogeneous regions of interest on the image. Such regions are then submitted to a further analysis (based both on heuristic rules and support vector classification) in order to reduce the false positives. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.
Correlation clustering for PRNU-based blind image source identification

We propose a new algorithm for blind camera identification, based on the Photo-Response Non-Uniformity (PRNU) noise estimated by image residuals. Successful identification relies on the correct clustering of residuals coming from the same camera. We adopt a two-step strategy. First, residuals are efficiently grouped by correlation clustering, setting parameters so as to over-partition the data points and avoid any wrong associations. Then, basic clusters are progressively merged by an ad hoc refinement algorithm. Experiments on the Dresden database prove the effectiveness of the proposed method.
A ground truth design tool for multiresolution images

We propose an interactive tool for designing ground-truth maps associated with multi-resolution remote sensing images. The target image is first segmented at object level by means of an edge-preserving algorithm. Then, a pre-classification defines groups of segments that are homogeneous both in spectral response and size. Finally, suitable candidate segments are selected and shown to the supervisor for inspection and labeling or possible rejection, in an iterative process, until the desired image covering is reached. Experimental results show that the proposed solution allows one to easily and quickly obtain ground-truth maps which are both locally and globally accurate, and where all classes are represented in a balanced manner.
Compression of SAR raw data via range focusing and trellis coded quantization

Recently, there has been a growing interest in the compression of SAR data. Unfortunately, raw data do not exhibit the statistical properties that allow for an efficient compression, and that are present, instead, in the focused data. On the other hand, focusing is too complex to be implemented on board. The authors propose to carry out only the low-complexity range focusing on the satellite, so as to restore part of the correlation and better concentrate the energy in a few samples. To exploit these properties, while limiting the computational complexity, they resort to a variable-rate encoding scheme based on trellis-coded vector quantization. Experiments on real SAR data show a satisfactory performance both in terms of rate-distortion results and of complexity.
Superpixel-based segmentation of remote sensing images through correlation clustering

In this paper a new object-oriented segmentation method for high-resolution remote sensing images is proposed. To limit computational complexity, a preliminary superpixel representation of the image is obtained by means of a suitable watershed transform. Then, a region adjacency graph is associated with the superpixels, with edge weights accounting for region similarity/dissimilarity. The final segmentation is then obtained by means of a graph-cutting approach, following a correlation clustering formulation. The optimal cut can be obtained by solving a Integer Linear Programming (ILP) problem, whose complexity, however, grows rapidly with the image size. Much faster near-optimal solutions are obtained, here, with a greedy solution. Experiments on a real-world high-resolution remote sensing image prove the potential of the approach.
Exploration of Multitemporal COSMO-SkyMed Data via Interactive Tree-Structured MRF Segmentation

We propose a new approach for remote sensing data exploration, based on a tight human-machine interaction. The analyst uses a number of powerful and user-friendly image classification/segmentation tools to obtain a satisfactory thematic map, based only on visual assessment and expertise. All processing tools are in the framework of the tree-structured MRF model, which allows for a flexible and spatially adaptive description of the data. We test the proposed approach for the exploration of multitemporal COSMO-SkyMed data, that we appropriately registered, calibrated, and filtered, obtaining a performance that is largely superior, in both subjective and objective terms, to that of comparable noninteractive methods.
Costs and advantages of shape-adaptive wavelet transform for region-based image coding

Region-based encoding techniques have been long investigated for the compression of still images and video sequences and have recently gained much popularity, as testified by the object-based nature of the MPEG-4 video coding standard. This work aims at analyzing costs and advantages of implementing such an approach by shape-adaptive wavelet transform and shape-adaptive SPIHT. The analysis of several performance measures in a number of experiments confirm the potential of wavelet-based region-based approach, and provide insight about what performance gains and losses can be expected in various operative conditions.
Multispectral-image coding by vector quantization with Kronecker-product representation

This paper proposes a new technique based on vector quantization (VQ) for very low bit-rate encoding of multispectral images. The new algorithm relies on the observation that in high spectral-resolution imagery the shape of a generic spatial block does not change significantly from band to band. Therefore, it is reasonable to represent each 3-D spatial/spectral block as the Kronecker product of a spatial-shape vector and a spectral-gain vector, and to jointly quantize only these representative vectors in place of the original block. Even though such an encoding strategy is suboptimal with respect to full-search VQ, the huge complexity reduction allows one to use much larger blocks and to better exploit the redundancy among close pixels of the image. Numerical experiments carried out on high spectral-resolution images show fully satisfactory results, with compression ratios exceeding 100:1, good image quality and very low encoding complexity.
Region-oriented compression of multispectral images by shape-adaptive wavelet transform and SPIHT

We present a new technique for the compression of remote-sensing hyperspectral images based on wavelet transform and zerotree coding of coefficients. In order to improve encoding efficiency, the image is first segmented in a small number of regions with homogeneous texture. Then, a shape-adaptive wavelet transform is carried out on each region and the resulting coefficients are finally encoded by a shape-adaptive version of SPIHT. Thanks to the segmentation map (sent as a side information) region boundaries are faithfully preserved and selective encoding strategies can be easily implemented. In addition, by-now homogeneous region textures can be more efficiently encoded.
SAR image simulation for the assessment of despeckling techniques

We propose a new framework for the quantitative assessment of SAR despeckling techniques, based on physical-level simulation of SAR images corresponding to canonical scenes. Thanks to the simulator, we can generate multiple SAR images of the same scene which differ only in the speckle content, and, hence, a true multilook SAR image, with an arbitrarily large number of looks, to use as “speckle-free” reference. Based on this concept, we select a small set of canonical scenes and, for each of them, a suitable set of objective measures which account for speckle suppression and image feature preservation. We gain insight into the system reliability by comparing the indications it gives for some sample despeckling filters with those obtained by expert visual inspection of the filtered images.
Contact Lens Detection and Classification in Iris Images through Scale Invariant Descriptor

We propose a new machine-learning technique for detecting the presence and type of contact lenses in iris images. Following the usual paradigm, we extract the regions of interest for classification, compute a feature vector based on local descriptors, and feed it to a properly trained SVM classifier. Major improvements w.r.t. Current state of the art concern the design of a more reliable segmentation procedure and the use of a recently proposed dense scale-invariant image descriptor. Experiments on publicly available datasets show the proposed method to outperform significantly all reference techniques.
Advances in texture-based segmentation of high resolution remote sensing imagery

The Texture Fragmentation and Reconstruction (TFR) algorithm, recently proposed for the segmentation of textured images, has been applied with promising results to high-resolution remote-sensing images. The algorithm provides a sequence of nested segmentation maps which allow the analysis at various scales of observation. Although for most test images TFR has proven able to recognize major semantic areas, some failures have also been observed due to the presence of large background regions that span the whole image and prevent the formation of distinct local textures. In this paper we introduce a new step in the TFR processing flow which detects background regions and divides them in multiple homogeneous fragments based on their geometric level properties. To this end, connected regions are first reduced to atomic components through a watershed-like transform, and then clustered again based on the features of the associated region-adjacency graph. Early experimental results prove the effectiveness of the new processing step, and its beneficial effect on the whole algorithm.
Kronecker-product gain-shape vector quantization for multispectral and hyperspectral image coding

This paper proposes a new vector quantization based (VQ-based) technique for very low bit rate encoding of multispectral images. We rely on the assumption that the shape of a generic spatial block does not change significantly from band to band, as is the case for high spectral-resolution imagery. In such a hypothesis, it is possible to accurately quantize a three-dimensional (3-D) block-composed of homologous two-dimensional (2-D) blocks drawn from several bands-as the Kronecker-product of a spatial-shape codevector and a spectral-gain codevector, with significant computation saving with respect to straight VQ. An even higher complexity reduction is obtained by representing each 3-D block in its minimum-square-error Kronecker-product form and by quantizing the component shape and gain vectors. For the block sizes considered, this encoding strategy is over 100 times more computationally efficient than unconstrained VQ, and over ten times more computationally efficient than direct gain-shape VQ. The proposed technique is obviously suboptimal with respect to VQ, but the huge complexity reduction allows one to use much larger blocks than usual and to better exploit both the statistical and psychovisual redundancy of the image. Numerical experiments show fully satisfactory results whenever the shape-invariance hypothesis turns out to be accurate enough, as in the case of hyperspectral images. In particular, for a given level of complexity and image quality, the compression ratio is up to five times larger than that provided by ordinary VQ, and also larger than that provided by other techniques specifically designed for multispectral image coding.
Tree-structured product-codebook vector quantization

This paper presents a new compression technique based on vector quantization. The proposed encoding algorithm uses a product codebook in which both component codebooks are tree-structured. This allows us to obtain low-complexity progressive encoding of large vectors. We propose a simple, greedy, procedure for the codebook design and assess its performance in the case of gray-scale images for gain-shape product VQ. Simulation results show that the proposed technique exhibits a significant performance gain at very low bit rates, while assuring a satisfactory image quality.
Splicebuster: A new blind image splicing detector

We propose a new feature-based algorithm to detect image splicings without any prior information. Local features are computed from the co-occurrence of image residuals and used to extract synthetic feature parameters. Splicing and host images are assumed to be characterized by different parameters. These are learned by the image itself through the expectation-maximization algorithm together with the segmentation in genuine and spliced parts. A supervised version of the algorithm is also proposed. Preliminary results on a wide range of test images are very encouraging, showing that a limited-size, but meaningful, learning set may be sufficient for reliable splicing localization.
Compression of SAR data through range focusing and variable-rate vector quantization

In spaceborne SAR systems, some form of data compression is required to reduce the bandwidth of the downlink channel. Compressing the raw data is very inefficient because such data exhibit little or no correlation. Data focusing would restore the original dependencies of the image, but it is too complex to be implemented onboard. In this paper we propose to carry out a partial, low-complexity focusing onboard, and then compress the resulting data by means of a suitable algorithm based on variable-rate adaptive vector quantization. Experiments show a performance improvement of 0.5-0.7 dB over compression techniques proposed in the literature.
Sequential Bayesian segmentation of remote sensing images

We present a fast Bayesian algorithm for the segmentation of remote-sensing images. It alternates two processing steps, the binary Bayesian segmentation of regions, and the separation of non-connected same-class regions, which both present relatively low complexity. As a result, a detailed and reliable K-region segmentation map can be obtained in limited CPU-time. In addition, the map is organized in a tree-structure (not necessarily binary) which helps gaining insight about the meaning of component regions.
Effects of despeckling on the estimation of fractal dimension from SAR images

The estimation of the fractal dimension of a natural surface using spectral analysis of amplitude SAR data is a powerful tool for geophysical applications. However, the obtained estimates are influenced by the presence of speckle. In this paper we present first results regarding the analysis of the effects on fractal dimension maps due to the application of despeckling techniques on SAR images. The use of simulated data allows to obtain also multilook images, presenting a very high number of looks, which should represent a virtually speckle-free image. In the results section different despeckling techniques are considered, relaying on both classical spatial filtering and non-local means. The presented results allow to draw meaningful conclusions on the effects of speckle and despeckling on spectral estimation and fractal dimension retrieving.
Advances in the segmentation and compression of multispectral images

Presents a new low-complexity technique for the segmentation of multispectral images, based on the use of a tree-structured Markov random field model. The image is associated with a binary tree, and is segmented recursively through a sequence of local splits based on a maximum a posteriori probability rule. To improve the reliability of the process, merging of nodes is now considered besides splitting, so as to allow for the re-shaping of incorrect region boundaries. Experimental results show that the new algorithm increases the fitness of the segmentation to the actual features of the image.
Embedded zerotree wavelet coding of multispectral images

We propose two algorithms based on the embedded zerotree wavelet approach to encode multispectral images. The Said-Pearlman algorithm is adapted to the case of multispectral images by considering spectral vectors of pixels rather than isolated pixels and by carrying out either VQ or KLT on these vectors, so as to take into account interband dependencies. Numerical experiments on a sample multispectral image show a very good performance for both algorithms.
The advantage of segmentation in SAR image compression

SAR images are severely degraded by speckle, and filtering is therefore a common practice. Filtering is especially useful before compression, to avoid spending valuable resources to represent noise; unfortunately, it also degrades important image features, like region boundaries. To overcome this problem, one can resort to a segmentation-based compression scheme, which allows one to preserve region boundaries, carry out intense denoising, and improve overall performance. In this work we assess the potential of segmentation-based compression through controlled experiments on synthetic SAR images. Numerical results seem to confirm the validity of this approach.
PRNU-based detection of small-size image forgeries

The Photo-Response Non-Uniformity (PRNU) has been recently introduced [1, 2] as a powerful tool to detect image forgeries. In spite of its effectiveness in many scenarios, the proposed method fails to detect small manipulations. In this work we propose a modified version of the detection algorithm described in , based on a preliminary segmentation of the image, which guarantees a better detection performance for small size additive forgeries.
Non-local methods for filtering interferometric SAR datasets

Multipass DInSAR processing is a well established technique that allows accurate 3D reconstruction and monitoring of the Earth surface. Many multipass techniques, such as the Small Baseline Subset approach, carry out a spatial multilooking of interferograms to counteract the decorrelation effects and the thermal noise and, also, to reduce computational complexity through spatial subsampling, thus enabling large scale (even full frame) analysis. Multilooking, however, degrades the spatial resolution w.r.t the original full resolution SAR product and therefore does not preserve contours of structures and in general boundaries of areas characterized by different backscattering. In this paper we propose the use of more sophisticated non-local filtering algorithms for the implementation of the interferometric multilook operation to guarantee the same noise rejection power of multilooking, while preserving high-resolution detail. Different strategies are considered and discussed for the selection of the best predictor pixels. Results on real data acquired by the ENVISAT mission demonstrate the good performance of the proposed nonlocal filtering methods.
Region-Based Transform Coding of Multispectral Images

We propose a new efficient region-based scheme for the compression of multispectral remote-sensing images. The region-based description of an image comprises a segmentation map, which singles out the relevant regions and provides their main features, followed by the detailed (possibly lossless) description of each region. The map conveys information on the image structure and could even be the only item of interest for the user; moreover, it enables the user to perform a selective download of the regions of interest, or can be used for high-level data mining and retrieval applications. This approach, with the multiple pieces of information required, may seem inherently inefficient. The goal of this research is to show that, by carefully selecting the appropriate segmentation and coding tools, region-based compression of multispectral images can be also effective in a rate-distortion sense, thus providing an image description that is both insightful and efficient. To this end, we define a generic coding scheme, based on Bayesian image segmentation and on transform coding, where several key design choices, however, are left open for optimization, from the type of transform, to the rate allocation procedure, and so on. Then, through an extensive experimental phase on real-world multispectral images, we gain insight on such key choices, and finally single out an efficient and robust coding scheme, with Bayesian segmentation, class-adaptive Karhunen-Loeve spectral transform, and shape-adaptive wavelet spatial transform, which outperforms state-of-the-art and carefully tuned conventional techniques, such as JPEG-2000 multicomponent or SPIHT-based coders.
Segmentation of remote-sensing images by supervised TS-MRF

In this work we specialize the recently proposed tree-structured MRF model to supervised segmentation of multispectral satellite images. This model allows a hierarchical representation of a 2-D field by means of a sequence of binary MRFs, each corresponding to a node in the tree. One can fit the intrinsic structure of the data to this tree-structured model, thereby defining a multi-parameter, flexible, MRF. Although a global MRF model is defined on the whole tree, optimization as well as estimation is carried out node by node, with a significant reduction in complexity. Experiments on a test SPOT image prove the superior performance of the algorithm w.r.t. other MRF-based or variational algorithms for supervised segmentation.
Training convolutional neural networks for semantic classification of remote sensing imagery

We explore the use of convolutional neural networks for the semantic classification of remote sensing images. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that that only need to be fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on three remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which provides a state-of-the-art performance.