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  • 1
    Language: English
    In: IEEE transactions on image processing, 2016-06, Vol.25 (6), p.2882-2895
    Description: Nonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper, we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high-resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradient operators and demonstrate the suitability of this algorithm through the comparison with the state-of-the-art approaches. Four data sets acquired by the Pleiades, Worldview-2, Ikonos, and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor.
    Subject(s): Algorithm design and analysis ; Rendering (computer graphics) ; Satellites ; Spatial resolution ; Data integration ; Remote sensing ; Usage ; Image processing ; Analysis ; Computer graphics ; Engineering Sciences ; Signal and Image processing
    ISSN: 1057-7149
    E-ISSN: 1941-0042
    Source: IEEE Electronic Library (IEL)
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 2
    Language: English
    In: IEEE geoscience and remote sensing letters, 2014-05, Vol.11 (5), p.930-934
    Description: The pansharpening process has the purpose of building a high-resolution multispectral image by fusing low spatial resolution multispectral and high-resolution panchromatic observations. A very credited method to pursue this goal relies upon the injection of details extracted from the panchromatic image into an upsampled version of the low-resolution multispectral image. In this letter, we compare two different injection methodologies and motivate the superiority of contrast-based methods both by physical consideration and by numerical tests carried out on remotely sensed data acquired by IKONOS and Quickbird sensors.
    Subject(s): Algorithm design and analysis ; pansharpening ; injection models ; Sensor phenomena and characterization ; High pass modulation ; Indexes ; Spatial resolution ; Remote sensing ; modulation transfer functions ; Engineering Sciences ; Computer Science ; Signal and Image processing
    ISSN: 1545-598X
    E-ISSN: 1558-0571
    Source: IEEE Electronic Library (IEL)
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  • 3
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2017-02, Vol.55 (2), p.753-766
    Description: Pansharpened images are widely used synthetic representations of the Earth surface characterized by both a high spatial resolution and a high spectral diversity. They are usually generated by extracting spatial details from a high-resolution PANchromatic image and by injecting them into a low spatial resolution multispectral image. The details injection is performed through injection coefficients, whose values can be either uniform for the whole image (global methods) or spatially variant (context-adaptive (CA) approaches). In this paper, we propose a CA approach in which the injection coefficients are estimated over image segments achieved through a binary partition tree segmentation algorithm. The approach is applied to two credited pansharpening algorithms based on the Gram-Schmidt orthogonalization procedure and the generalized Laplacian pyramid technique. The performance assessment is performed using two different data sets acquired by the QuickBird and the WorldView-3 satellites. The validation procedure, both at full and at reduced resolution, shows the suitability of the proposed approach, which reaches a good tradeoff between accuracy and computational burden.
    Subject(s): Earth ; pansharpening ; Image segmentation ; Satellites ; image fusion ; remote sensing ; Partitioning algorithms ; Binary partition tree (BPT) ; Indexes ; Spatial resolution ; context-adaptive (CA) algorithms ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 4
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2010-10, Vol.48 (10), p.3747-3762
    Description: Morphological attribute profiles (APs) are defined as a generalization of the recently proposed morphological profiles (MPs). APs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of the structural information. According to the type of the attributes considered in the morphological attribute transformation, different parametric features can be modeled. The generation of APs, thanks to an efficient implementation, strongly reduces the computational load required for the computation of conventional MPs. Moreover, the characterization of the image with different attributes leads to a more complete description of the scene and to a more accurate modeling of the spatial information than with the use of conventional morphological filters based on a predefined structuring element. Here, the features extracted by the proposed operators were used for the classification of two very high resolution panchromatic images acquired by Quickbird on the city of Trento, Italy. The experimental analysis proved the usefulness of APs in modeling the spatial information present in the images. The classification maps obtained by considering different APs result in a better description of the scene (both in terms of thematic and geometric accuracy) than those obtained with an MP.
    Subject(s): morphological profiles (MPs) ; Image resolution ; remote sensing ; object detection ; Information filtering ; Data mining ; very high resolution (VHR) images ; Image analysis ; Layout ; Classification ; Lead ; Cities and towns ; morphological attribute profiles (APs) ; Information filters ; Feature extraction ; mathematical morphology ; Spatial resolution ; Internal geophysics ; Earth, ocean, space ; Applied geophysics ; Earth sciences ; Exact sciences and technology ; Evaluation ; Resolution (Optics) ; Image processing ; Analysis ; Detectors ; Design and construction ; Remote sensing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 5
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2017-08, Vol.55 (8), p.4567-4585
    Description: It is now possible to collect hyperspectral video sequences at a near real-time frame rate. The wealth of spectral, spatial, and temporal information of those sequences is appealing for various applications, but classical video processing techniques must be adapted to handle the high dimensionality and huge size of the data to process. In this paper, we introduce a novel method based on the hierarchical analysis of hyperspectral video sequences to perform object tracking. This latter operation is tackled as a sequential object detection process, conducted on the hierarchical representation of the hyperspectral video frames. We apply the proposed methodology to the chemical gas plume tracking scenario and compare its performances with state-of-the-art methods, for two real hyperspectral video sequences, and show that the proposed approach performs at least equally well.
    Subject(s): Shape ; hyperspectral video sequence ; Video sequences ; Object detection ; Binary partition tree ; gas plume tracking ; Object tracking ; Chemicals ; Hyperspectral imaging ; Video equipment ; Usage ; Research ; Plumes (Fluid dynamics) ; Remote sensing ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 6
    Language: English
    In: IEEE transactions on image processing, 2018-09, Vol.27 (9), p.4330-4344
    Description: Pansharpening is an important application in remote sensing image processing. It can increase the spatial-resolution of a multispectral image by fusing it with a high spatial-resolution panchromatic image in the same scene, which brings great favor for subsequent processing such as recognition, detection, etc. In this paper, we propose a continuous modeling and sparse optimization based method for the fusion of a panchromatic image and a multispectral image. The proposed model is mainly based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). In addition, we also propose a Toeplitz sparse term for representing the correlation of adjacent bands. The model is convex and solved by the alternating direction method of multipliers which guarantees the convergence of the proposed method. Extensive experiments on many real datasets collected by different sensors demonstrate the effectiveness of the proposed technique as compared with several state-of-the-art pansharpening approaches.
    Subject(s): RKHS ; Image edge detection ; Two dimensional displays ; Heaviside ; Pansharpening ; Electronic mail ; Toeplitz sparsity ; Multiresolution analysis ; alternating direction method of multipliers ; Sensors ; remote sensing image ; Spatial resolution ; sparse model ; Engineering Sciences ; Signal and Image processing
    ISSN: 1057-7149
    E-ISSN: 1941-0042
    Source: IEEE Electronic Library (IEL)
    Source: © ProQuest LLC All rights reserved〈img src="https://exlibris-pub.s3.amazonaws.com/PQ_Logo.jpg" style="vertical-align:middle;margin-left:7px"〉
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  • 7
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2014-08, Vol.52 (8), p.5122-5136
    Description: In recent years, sparse representations have been widely studied in the context of remote sensing image analysis. In this paper, we propose to exploit sparse representations of morphological attribute profiles for remotely sensed image classification. Specifically, we use extended multiattribute profiles (EMAPs) to integrate the spatial and spectral information contained in the data. EMAPs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of structural information. Although the EMAPs' feature vectors may have high dimensionality, they lie in class-dependent low-dimensional subpaces or submanifolds. In this paper, we use the sparse representation classification framework to exploit this characteristic of the EMAPs. In short, by gathering representative samples of the low-dimensional class-dependent structures, any given sample may by sparsely represented, and thus classified, with respect to the gathered samples. Our experiments reveal that the proposed approach exploits the inherent low-dimensional structure of the EMAPs to provide state-of-the-art classification results for different multi/hyperspectral data sets.
    Subject(s): Training ; Dictionaries ; Feature extraction ; Vectors ; remote sensing image classification ; Kernel ; Hyperspectral imaging ; Extended multiattribute profiles (EMAPs) ; sparse representation ; Internal geophysics ; Earth, ocean, space ; Applied geophysics ; Earth sciences ; Exact sciences and technology ; Usage ; Mathematical models ; Image processing ; Kernel functions ; Remote sensing ; Innovations ; Signal and Image Processing ; Computer Science
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 8
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2014-10, Vol.52 (10), p.6062-6074
    Description: Including spatial information is a key step for successful remote sensing image classification. In particular, when dealing with high spatial resolution, if local variability is strongly reduced by spatial filtering, the classification performance results are boosted. In this paper, we consider the triple objective of designing a spatial/spectral classifier, which is compact (uses as few features as possible), discriminative (enhances class separation), and robust (works well in small sample situations). We achieve this triple objective by discovering the relevant features in the (possibly infinite) space of spatial filters by optimizing a margin-maximization criterion. Instead of imposing a filter bank with predefined filter types and parameters, we let the model figure out which set of filters is optimal for class separation. To do so, we randomly generate spatial filter banks and use an active-set criterion to rank the candidate features according to their benefits to margin maximization (and, thus, to generalization) if added to the model. Experiments on multispectral very high spatial resolution (VHR) and hyperspectral VHR data show that the proposed algorithm, which is sparse and linear, finds discriminative features and achieves at least the same performances as models using a large filter bank defined in advance by prior knowledge.
    Subject(s): Support vector machines ; Training ; Attribute profiles ; very high resolution ; texture ; hyperspectral ; Feature extraction ; mathematical morphology ; Spatial resolution ; Optimization ; feature selection ; Principal component analysis ; Usage ; Algorithms ; Analysis ; Remote sensing ; Spatial systems ; Computer science ; Cognitive science
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 9
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2017-12, Vol.55 (12), p.6950-6963
    Description: With the development of hyperspectral sensors, nowadays, we can easily acquire large amount of hyperspectral images (HSIs) with very high spatial resolution, which has led to a better identification of relatively small structures. Owing to the high spatial resolution, there are much less mixed pixels in the HSIs, and the boundaries between these categories are much clearer. However, the high spatial resolution also leads to complex and fine geometrical structures and high inner-class variability, which make the classification results very "noisy." In this paper, we propose a multimorphological superpixel (MMSP) method to extract the spectral and spatial features and address the aforementioned problems. To reduce the difference within the same class and obtain multilevel spatial information, morphological features (multistructuring element extended morphological profile or multiattribute filter extended multi-attribute profiles) are first obtained from the original HSI. After that, simple linear iterative clustering segmentation method is performed on each morphological feature to acquire the MMSPs. Then, uniformity constraint is used to merge the MMSPs belonging to the same class which can avoid introducing the information from different classes and acquire spatial structures at object level. Subsequently, mean filtering is utilized to extract the spatial features within and among MMSPs. At last, base kernels are obtained from the spatial features and original HSI, and several multiple kernel learning methods are used to obtain the optimal kernel to incorporate into the support vector machine. Experiments conducted on three widely used real HSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.
    Subject(s): Shape ; Hyperspectral images (HSIs) ; multimorphological ; Feature extraction ; superpixel ; Data mining ; Kernel ; Spatial resolution ; spatial–spectral classification ; Hyperspectral imaging ; Image processing ; Kernel functions ; Usage ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 10
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2016-05, Vol.54 (5), p.3083-3102
    Description: Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples.
    Subject(s): textural features ; Image edge detection ; multiple morphological component analysis (MMCA) ; Transforms ; Feature extraction ; Decomposition ; image separation ; Hyperspectral imaging ; multinomial logistic regression (MLR) ; sparse representation
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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