A Three-Dimensional Model-Based Approach to the Estimation of the Tree Top Height by Fusing Low-Density LiDAR Data and Very High Resolution Optical Images

Paris, Claudia; Bruzzone, Lorenzo

doi:10.1109/tgrs.2014.2324016

Cited by 43 publications

(28 citation statements)

References 30 publications

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“…Claudia Paris et al in [25], based on the fusion between low-density LiDAR (Light detection and ranging) data and high-resolution optical images proposes a 3-D model-based approach to the estimate tree top height as one of the forest attributes. While, Wenzhi Liao et al in [26] propose a generalized graph-based fusion method to couple dimension reduction and feature fusion of hyperspectral and LiDAR data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Image Fusion in Remote Sensing Applications: A Review

Pandit¹,

Bhiwani²

2015

IJCA

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Major technical constraints like minimum data storage at satellite platform in space, less bandwidth for communication with earth station, etc. limits the satellite sensors from capturing images with high spatial and high spectral resolutions simultaneously. To overcome this limitation, image fusion has proved to be a potential tool in remote sensing applications which integrates the information from combinations of panchromatic, multispectral or hyperspectral images; intended to result in a composite image having both higher spatial and higher spectral resolutions. The research in this area cites date back to last few decades, but the diverse approaches proposed so far by different researchers have been rarely discussed at one place. This paper is an honest attempt to collectively discuss all possible algorithms along with quality metrics following two assessment procedures i.e. at full and reduced scale resolutions to evaluate performance of these algorithms. General TermsRemote sensing, Image fusion, Satellite image processing.

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Section: Literature Reviewmentioning

confidence: 99%

“…It is a more credited global index proposed for pansharpening as follows: (27) Where RMSE is defined as (25) …”

Section: Errur Relative Globale Adimensionnelle De Synthèse (Ergas)mentioning

confidence: 99%

Image Fusion in Remote Sensing Applications: A Review

Pandit¹,

Bhiwani²

2015

IJCA

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“…They help greatly to make a decision. The application fields of the data fusion are varied and diverse: Medical imaging (Magtibay et al, 2016;Adali et al, 2015), economy (Barenboim and Pearl, 2016), information theory (Gagolewski, 2016), image processing (Paris and Bruzzone, 2015), etc. In remote sensing where the nature and the resolution of sensors are various and different, methods of image fusion implement several types of images: Panchromatic (PAN), Multispectral (MS), Hyperspectral (HS) and Radar (SAR).…”

Section: Introductionmentioning

confidence: 99%

Application of Morphological Component Analysis to Optical Image Fusion

Loum¹,

Armand²,

Ghislain³

et al. 2017

American Journal of Applied Sciences

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Abstract:The image fusion technique is widely used in remote sensing. Its purpose is to provide comprehensive information without arte facts by combining the partial information from different source images. In this study, we propose a new model of images fusion with very high spatial resolution. We use the separation capacities of the Morphological Component Analysis (MCA) to extract the smooth and texture components of our images. These morphological components are then fused separately using the decomposition in the Laplacian pyramids for the smooth part and bivariate Hahn polynomials for texture part. Finally the image fusion is obtained through linear combination of merged smooth and texture components. The experiments carried out on IKONOS, LANDSAT and Quick Bird remote sensing images show the good performances of our method which has been compared to conventional methods. The performances obtained in our experiments are characterized by a small global metric such as ERGAS equals to 3.88 for IKONOS image and 3.65 for QuickBird image compared to 8.70 for IKONOS image and 6.97 for QuickBird for conventional HIS algorithms. We also have a mean loss of 15% for spectral information com pare to those of the conventional methods which revolve around 25%. The degradation of spatial information in order of 17% in contrast to conventional HIS algorithms which oscillate around 21%.

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“…Many techniques have been developed for fusion of these heterogeneous features in a classification task. Summing these fusion strategies up, they can be broadly divided into five categories: based on the feature stack structure (Puttonen et al, 2011), hierarchical scheme (Paris and Bruzzone, 2015), sparse representation (Zhang and Prasad, 2016), manifold learning or graphs (Gu and Wang, 2017), and multiple kernel learning (MKL) (Gu and Wang, 2015). Koetz et al (2007) classified fuel composition from fused LiDAR and hyperspectral bands using support vector machines (SVMs) and showed that the classification accuracies after fusion were higher than those based on either sensor alone.…”

Section: Introductionmentioning

confidence: 99%

“…Fusion strategies based on hierarchical scheme for HSI and LiDAR data firstly process one data source in a classifier and then integrate its output with another data source to obtain the final results. A 3-D model-based approach was proposed to the estimation of the tree top height based on the fusion between low-density LiDAR data and high-resolution optical images (Paris and Bruzzone, 2015). In their proposed approach, the integration of the two remote sensing data sources was first exploited to accurately detect and delineate the single tree crowns.…”

Section: Introductionmentioning

confidence: 99%

Class-Pair-Guided Multiple Kernel Learning of Integrating Heterogeneous Features for Classification

Wang

Liu

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In recent years, many studies on remote sensing image classification have shown that using multiple features from different data sources can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. The conventional combined kernel learned by MKL can be regarded as the compromise of all basic kernels for all classes in classification. It is the best of the whole, but not optimal for each specific class. For this problem, this paper proposes a class-pair-guided MKL method to integrate the heterogeneous features (HFs) from multispectral image (MSI) and light detection and ranging (LiDAR) data. In particular, the "one-against-one" strategy is adopted, which converts multiclass classification problem to a plurality of two-class classification problem. Then, we select the best kernel from pre-constructed basic kernels set for each class-pair by kernel alignment (KA) in the process of classification. The advantage of the proposed method is that only the best kernel for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on two real data sets, and the experimental results show that the proposed method achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms.

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A Three-Dimensional Model-Based Approach to the Estimation of the Tree Top Height by Fusing Low-Density LiDAR Data and Very High Resolution Optical Images

Cited by 43 publications

References 30 publications

Image Fusion in Remote Sensing Applications: A Review

Image Fusion in Remote Sensing Applications: A Review

Application of Morphological Component Analysis to Optical Image Fusion

Class-Pair-Guided Multiple Kernel Learning of Integrating Heterogeneous Features for Classification

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