Journal Article10.1109/TGRS.2007.912445
Multidimensional Probability Density Function Matching for Preprocessing of Multitemporal Remote Sensing Images
TL;DR: This paper addresses the problem of matching the statistical properties of the distributions of two (or more) multi-spectral remote sensing images acquired on the same geographical area at different times by properly taking into account the correlation among spectral channels.
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Abstract: This paper addresses the problem of matching the statistical properties of the distributions of two (or more) multi-spectral remote sensing images acquired on the same geographical area at different times. An N-D probability density function (pdf) matching technique for the preprocessing of multitemporal images is introduced in the remote sensing domain by defining and analyzing three important application scenarios: 1) supervised classification; 2) partially supervised classification; and 3) change detection. Unlike other methods adopted in remote sensing applications, the procedure considered performs the matching process by properly taking into account the correlation among spectral channels, thus retaining the data correlation structure after the pdf matching. Experimental results obtained on real multitemporal remote sensing data sets confirm the validity of the presented technique in all the considered scenarios.
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Citations
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.
756
Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances
TL;DR: A critical review of the recent advances in DA approaches for remote sensing is provided and an overview of DA methods divided into four categories: 1) invariant feature selection, 2) representation matching, 3) adaptation of classifiers, and 4) selective sampling are presented.
413
A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images
Lorenzo Bruzzone,Francesca Bovolo +1 more
- 01 Mar 2013
TL;DR: A framework that aims at defining a top-down approach to the design of the architecture of novel change-detection systems for multitemporal VHR images taking into account the intrinsic complexity associated with these data is proposed.
342
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
TL;DR: In this paper, the authors review the main advances for hyperspectral remote sensing image classification through illustrative examples, including user interaction via active learning, to take advantage of the manifold structure with semisupervised learning, extract and encode invariances, or to adapt classifiers and image representations to unseen yet similar scenes.
289
Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data.
TL;DR: In this article, a critical review of the recent advances in Domain Adaptation (DA) for remote sensing data classification is presented, as well as examples of application of the considered techniques to real remote sensing images characterized by very high spatial and spectral resolution.
226
References
Review Article Digital change detection techniques using remotely-sensed data
TL;DR: An evaluation of results indicates that various procedures of change detection produce different maps of change even in the same environment.
3.9K
•Book
Remote Sensing Digital Image Analysis: An Introduction
John A. Richards
- 01 Jan 2008
TL;DR: In this paper, the authors present an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.
3.7K
Color transfer between images
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