A Two-Stage PAN-Sharpening Algorithm Based on Sparse Representation for Spectral Distortion Reduction

2021 ◽  
pp. 2250007
Author(s):  
Rajesh Gogineni ◽  
Dhara J Sangani
Keyword(s):  
Sparse Representation ◽  
Spatial Information ◽  
Spectral Information ◽  
Technological Advancement ◽  
Modulation Transfer ◽  
Two Stage ◽  
Sensing Applications ◽  
Fused Image ◽  
Iterative Filtering ◽  
Distortion Reduction

Inspite of technological advancement, inherent processing capability of current age sensors limits the desired details in the acquired image for variety of remote sensing applications. Pan-sharpening is a prominent scheme to integrate the essential spatial details inferred from panchromatic (PAN) image and the desired spectral information of multispectral (MS) image. This paper presents an effective two-stage pan-sharpening method to produce high resolution multispectral (HRMS) image. The proposed method is based on the premise that the HRMS image can be formulated as an amalgam of spectral and spatial components. The spectral components are estimated by processing the interpolated MS image with a filter approximated with modulation transfer function (MTF) of the sensor. Sparse representation theory is adapted to construct the spatial components. The high-frequency details extracted from the PAN image and its low resolution variant are utilized to construct dual dictionaries. The dictionaries are jointly learned by an efficient training algorithm to enhance the adaptability. The hypothesis of sparse coefficients invariance over scales is also incorporated to reckon the appropriate spatial information. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four distinct datasets generated from QuickBird, IKONOS, Pléiades and WorldView-2 sensors are used for experimentation. The comprehensive assessment at reduced-scale and full-scale persuade the effectiveness of proposed method in the retention of spectral information and intensification of the spatial details.

scholarly journals Remote Sensing Image Fusion Based on Sparse Representation and Guided Filtering

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 303 ◽  
Author(s):  
Xiaole Ma ◽  
Shaohai Hu ◽  
Shuaiqi Liu ◽  
Jing Fang ◽  
Shuwen Xu
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Sparse Representation ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Fusion Method ◽  
Remote Sensing Image Fusion ◽  
Guided Filtering ◽  
Fused Image ◽  
Image Fusion Method

In this paper, a remote sensing image fusion method is presented since sparse representation (SR) has been widely used in image processing, especially for image fusion. Firstly, we used source images to learn the adaptive dictionary, and sparse coefficients were obtained by sparsely coding the source images with the adaptive dictionary. Then, with the help of improved hyperbolic tangent function (tanh) and l 0 − max , we fused these sparse coefficients together. The initial fused image can be obtained by the image fusion method based on SR. To take full advantage of the spatial information of the source images, the fused image based on the spatial domain (SF) was obtained at the same time. Lastly, the final fused image could be reconstructed by guided filtering of the fused image based on SR and SF. Experimental results show that the proposed method outperforms some state-of-the-art methods on visual and quantitative evaluations.

scholarly journals Optimizing Sensor Relationship for Fusion of Multispectral and Panchromatic Imagery

2019 ◽  
Vol 9 (2) ◽  
pp. 3077-3083
Keyword(s):  
Remote Sensing ◽  
Optimization Approach ◽  
Modulation Transfer ◽  
Sensing Applications ◽  
Fused Image ◽  
Satellite Sensors ◽  
Assessment Techniques ◽  
Remote Sensing Applications ◽  
Imaging Sensors ◽  
Relationship Of

The remote sensing satellite products: multispectral and panchromatic imagery are characterized by different levels of spatio-spectral resolutions. The fusion of these two images (provided, they are acquired for same geographic scenario) is also known as ‘Pansharpening’. This produces a composite image featuring simultaneous high levels of spatio-spectral resolutions to meet the demand of the most of remote sensing applications. Thus, different approaches for such fusion and further its quality assessment are continuously researched. The modulation transfer function is unique to the imaging sensors. In this paper, the sensor relationship of the input imagery is optimized to produce the efficient pansharpened/fused image. The performance measurement is carried out on two real datasets made available by WorldView-2 and WorldView-3 satellite sensors using two assessment techniques. The results of optimization approach are further compared to nine different most recent fusion algorithms

scholarly journals A Pan-Sharpening Method Based on Evolutionary Optimization and IHS Transformation

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yingxia Chen ◽  
Guixu Zhang
Keyword(s):  
Performance Metrics ◽  
Spatial Information ◽  
Evolutionary Optimization ◽  
Transformation Model ◽  
New Approach ◽  
Sensing Applications ◽  
High Quality Image ◽  
Fused Image ◽  
Remote Sensing Applications ◽  
A New Technique

In many remote sensing applications, users usually prefer a multispectral image with both high spectral and high spatial information. This high quality image could be obtained by pan-sharpening techniques which fuse a high resolution panchromatic (PAN) image and a low resolution multispectral (MS) image. In this paper, we propose a new technique to do so based on the adaptive intensity-hue-saturation (IHS) transformation model and evolutionary optimization. The basic idea is to reconstruct the target image through a parameterized adaptive IHS transformation. An optimization objective is thus introduced by considering the relations between the fused image and the original PAN and MS images. The control parameters are optimized by an evolutionary algorithm. Experimental results show that our new approach is practical and performs much better than some state-of-the-art techniques according to the performance metrics.

scholarly journals Sparse Representation Graph for Hyperspectral Image Classification Assisted by Class Adjusted Spatial Distance

2020 ◽  
Vol 10 (21) ◽  
pp. 7740
Author(s):  
Wanghao Xu ◽  
Siqi Luo ◽  
Yunfei Wang ◽  
Youqiang Zhang ◽  
Guo Cao
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spatial Distance ◽  
Spectral Information ◽  
Hyperspectral Image Classification ◽  
Distance Information ◽  
Training Samples ◽  
Two Samples

In the past few years, the sparse representation (SR) graph-based semi-supervised learning (SSL) has drawn a lot of attention for its impressive performance in hyperspectral image classification with small numbers of training samples. Among these methods, the probabilistic class structure regularized sparse representation (PCSSR) approach, which introduces the probabilistic relationship between samples into the SR process, has shown its superiority over state-of-the-art approaches. However, this category of classification methods only apply another SR process to generate the probabilistic relationship, which focuses only on the spectral information but fails to utilize the spatial information. In this paper, we propose using the class adjusted spatial distance (CASD) to measure the distance between each two samples. We incorporate the proposed a CASD-based distance information into PCSSR mode to further increase the discriminability of original PCSSR approach. The proposed method considers not only the spectral information but also the spatial information of the hyperspectral data, consequently leading to significant performance improvement. Experimental results on different datasets demonstrate that compared with state-of-the-start classification models, the proposed method achieves the highest overall accuracies of 99.71%, 97.13%, and 97.07% on Botswana (BOT), Kennedy Space Center (KSC) and the truncated Indian Pines (PINE) datasets, respectively, with a small number of training samples selected from each class.

Analysis of Sparse Representation and Blind Source Separation

2004 ◽  
Vol 16 (6) ◽  
pp. 1193-1234 ◽  
Author(s):  
Yuanqing Li ◽  
Andrzej Cichocki ◽  
Shun-ichi Amari
Keyword(s):  
Sparse Representation ◽  
Blind Source Separation ◽  
Clustering Algorithm ◽  
Source Separation ◽  
Coefficient Matrix ◽  
Data Matrix ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Basis Matrix ◽  
Two Stage

In this letter, we analyze a two-stage cluster-then-l1-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l1 norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l1—norm solution and the l0—norm solution is also analyzed according to a probabilistic framework. If the obtained l1—norm solution is sufficiently sparse, then it is equal to the l0—norm solution with a high probability. Furthermore, the l1—norm solution is robust to noise, but the l0—norm solution is not, showing that the l1—norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

2015 ◽  
Vol XL-7/W4 ◽  
pp. 197-201 ◽  
Author(s):  
Y. Yang ◽  
H. T. Li ◽  
Y. S. Han ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Graph Theory ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Sensing Applications ◽  
Initial Object ◽  
Object Layer ◽  
Evolution Approach

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

scholarly journals Przyszłość systemów rejestracji nieruchomości wobec rozwoju inteligentnych technologii. Wybrane problemy prawne na tle wniosków z konferencji „Common Vision Conference 2016. Migration to a Smart World”, Amsterdam, 5–7 czerwca 2016 roku

2017 ◽  
Vol 15 (2) ◽  
pp. 301-320
Author(s):  
Maria Kaczorowska
Keyword(s):  
Spatial Data ◽  
Data Exchange ◽  
Spatial Information ◽  
Smart Cities ◽  
Information Service ◽  
Advanced Technology ◽  
Technological Advancement ◽  
Land Registration ◽  
The Impact ◽  
Common Vision

The development of information technologies offers new possibilities of use of information collected in public registers, such as land registers and cadastres, which play a significant role in establishing the infrastructure for spatial information. Efficient use of spatial information systems with the purpose of a sustainable land management shall be based on en suring the interconnection of different information resources, data exchange, as well as a broad access to data. The role of land registration systems in the context of technological advancement was the subject of the Common Vision Conference 2016. Migration to a Smart World, held on 5–7 June 2016 in Amsterdam. The conference was organized by Europe’s five leading mapping, cadastre and land registry associations, cooperating within a “Common Vision” agreement: EuroGeographics, Permanent Committee on Cadastre, European Land Registries Association, European Land Information Service and Council of European Geodetic Surveyors. The discussion during the conference focused on topics regarding the idea of smart cities, marine cadastre, interoperability of spatial data, as well as the impact of land registers and cadastres on creating the infrastructure for spatial information and developing e-government, at both national and European levels. The paper aims to present an overview of issues covered by the conference and also to highlight some important problems arising from implementing advanced technology solutions in the field of land registration.

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