Journal ArticleDOI
Fusion of Hyperspectral and LiDAR Remote Sensing Data Using Multiple Feature Learning
TLDR
A new efficient strategy for fusion and classification of hyperspectral and LiDAR data designed to integrate multiple types of features extracted from these data, which does not require any regularization parameters.Abstract:
Hyperspectral image classification has been an active topic of research. In recent years, it has been found that light detection and ranging (LiDAR) data provide a source of complementary information that can greatly assist in the classification of hyperspectral data, in particular when it is difficult to separate complex classes. This is because, in addition to the spatial and the spectral information provided by hyperspectral data, LiDAR can provide very valuable information about the height of the surveyed area that can help with the discrimination of classes and their separability. In the past, several efforts have been investigated for fusion of hyperspectral and LiDAR data, with some efforts driven by the morphological information that can be derived from both data sources. However, a main challenge for the learning approaches is how to exploit the information coming from multiple features. Specifically, it has been found that simple concatenation or stacking of features such as morphological attribute profiles (APs) may contain redundant information. In addition, a significant increase in the number of features may lead to very high-dimensional input features. This is in contrast with the limited number of training samples often available in remote-sensing applications, which may lead to the Hughes effect. In this work, we develop a new efficient strategy for fusion and classification of hyperspectral and LiDAR data. Our approach has been designed to integrate multiple types of features extracted from these data. An important characteristic of the presented approach is that it does not require any regularization parameters, so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a hyperspectral image and a LiDAR-derived digital surface model (DSM) collected over the University of Houston campus and the neighboring urban area, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results.read more
Citations
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Journal ArticleDOI
Multisource Remote Sensing Data Classification Based on Convolutional Neural Network
TL;DR: The classification fusion of hyperspectral imagery (HSI) and data from other multiple sensors, such as light detection and ranging (LiDAR) data, is investigated with the state-of-the-art deep learning, named the two-branch convolution neural network (CNN).
Journal ArticleDOI
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art
Pedram Ghamisi,Behnood Rasti,Naoto Yokoya,Qunming Wang,Bernhard Höfle,Lorenzo Bruzzone,Francesca Bovolo,Mingmin Chi,Katharina Anders,Richard Gloaguen,Peter M. Atkinson,Jon Atli Benediktsson +11 more
TL;DR: An increase in remote sensing and ancillary data sets opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand.
Journal ArticleDOI
Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN
TL;DR: An unsupervised feature extraction framework, named as patch-to-patch convolutional neural network (PToP CNN), is proposed for collaborative classification of hyperspectral and LiDAR data and provides superior performance when compared with some state-of-the-art classifiers, such as two-branch CNN and context CNN.
Journal ArticleDOI
Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network
TL;DR: A novel framework for the fusion of hyperspectral and light detection and ranging-derived rasterized data using extinction profiles (EPs) and deep learning and results indicate that the proposed approach can achieve accurate classification results compared to other approaches.
Posted Content
Multisource and Multitemporal Data Fusion in Remote Sensing.
Pedram Ghamisi,Behnood Rasti,Naoto Yokoya,Qunming Wang,Bernhard Höfle,Lorenzo Bruzzone,Francesca Bovolo,Mingmin Chi,Katharina Anders,Richard Gloaguen,Peter M. Atkinson,Jon Atli Benediktsson +11 more
TL;DR: In this paper, the authors provide a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references.
References
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Journal ArticleDOI
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Jose M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul D. Gader,Jocelyn Chanussot +6 more
TL;DR: This paper presents an overview of un Mixing methods from the time of Keshava and Mustard's unmixing tutorial to the present, including Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixed algorithms.