Journal ArticleDOI
Morphological Attribute Profiles for the Analysis of Very High Resolution Images
TLDR
The classification maps obtained by considering different APs result in a better description of the scene than those obtained with an MP, and the usefulness of APs in modeling the spatial information present in the images is proved.Abstract:
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.read more
Citations
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Journal ArticleDOI
Hyperspectral Remote Sensing Data Analysis and Future Challenges
Jose M. Bioucas-Dias,Antonio Plaza,Gustau Camps-Valls,Paul Scheunders,Nasser M. Nasrabadi,Jocelyn Chanussot +5 more
TL;DR: A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.
Journal ArticleDOI
Advances in Spectral-Spatial Classification of Hyperspectral Images
TL;DR: Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper and several techniques are investigated for combining both spatial and spectral information.
Journal ArticleDOI
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods
TL;DR: In this paper, the authors focus on the challenging problem of hyperspectral image classification, which has recently gained in popularity and attracted the interest of other scientific disciplines such as machine learning, image processing, and computer vision.
Journal ArticleDOI
Graph Convolutional Networks for Hyperspectral Image Classification
TL;DR: A new minibatch GCN is developed that is capable of inferring out-of-sample data without retraining networks and improving classification performance, and three fusion strategies are explored: additive fusion, elementwise multiplicative fusion, and concatenation fusion to measure the obtained performance gain.
Journal ArticleDOI
Graph Convolutional Networks for Hyperspectral Image Classification
TL;DR: In this paper, a mini-batch graph convolutional network (called miniGCN) is proposed for hyperspectral image classification, which allows to train large-scale GCNs in a minibatch fashion.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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BookDOI
Assessing the accuracy of remotely sensed data : principles and practices
Russell G. Congalton,Kass Green +1 more
TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.
Book
Morphological Image Analysis: Principles and Applications
TL;DR: This self-contained volume will be valuable to all engineers, scientists, and practitioners interested in the analysis and processing of digital images.