Search results for "multispectral"
showing 10 items of 242 documents
Interactive Pansharpening and Active Classification in Remote Sensing
2013
This chapter presents two multimodal prototypes for remote sensing image classification where user interaction is an important part of the system. The first one applies pansharpening techniques to fuse a panchromatic image and a multispectral image of the same scene to obtain a high resolution (HR) multispectral image. Once the HR image has been classified the user can interact with the system to select a class of interest. The pansharpening parameters are then modified to increase the system accuracy for the selected class without deteriorating the performance of the classifier on the other classes. The second prototype utilizes Bayesian modeling and inference to implement active learning …
Cloud screening and multitemporal unmixing of MERIS FR data
2007
The operational use of MERIS images can be hampered by the presence of clouds. This work presents a cloud screening algorithm that takes advantage of the high spectral and radiometric resolutions of MERIS and the specific location of some of its bands to increase the cloud detection accuracy. Moreover, the proposed algorithm provides a per-pixel probabilistic map of cloud abundance rather than a binary cloud presence flag. In order to test the proposed algorithm we propose a cloud screening validation method based on temporal series. In addition, we evaluate the impact of the cloud screening in a multitemporal unmixing application, where a temporal series of MERIS FR images acquired over Th…
Cloud-screening algorithm for ENVISAT/MERIS multispectral images
2007
This paper presents a methodology for cloud screening of multispectral images acquired with the Medium Resolution Imaging Spectrometer (MERIS) instrument on-board the Environmental Satellite (ENVISAT). The method yields both a discrete cloud mask and a cloud-abundance product from MERIS level-lb data on a per-pixel basis. The cloud-screening method relies on the extraction of meaningful physical features (e.g., brightness and whiteness), which are combined with atmospheric-absorption features at specific MERIS-band locations (oxygen and watervapor absorptions) to increase the cloud-detection accuracy. All these features are inputs to an unsupervised classification algorithm; the cloud-proba…
Vector anisotropic filter for multispectral image denoising
2015
In this paper, we propose an approach to extend the application of anisotropic Gaussian filtering for multi- spectral image denoising. We study the case of images corrupted with additive Gaussian noise and use sparse matrix transform for noise covariance matrix estimation. Specifically we show that if an image has a low local variability, we can make the assumption that in the noisy image, the local variability originates from the noise variance only. We apply the proposed approach for the denoising of multispectral images corrupted by noise and compare the proposed method with some existing methods. Results demonstrate an improvement in the denoising performance.
Machine learning in remote sensing data processing
2009
Remote sensing data processing deals with real-life applications with great societal values. For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmental issues. To treat efficiently the acquired data and provide accurate products, remote sensing has evolved into a multidisciplinary field, where machine learning and signal processing algorithms play an important role nowadays. This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.
Dimensionality Reduction Techniques: An Operational Comparison On Multispectral Satellite Images Using Unsupervised Clustering
2006
Multispectral satellite imagery provides us with useful but redundant datasets. Using Dimensionality Reduction (DR) algorithms, these datasets can be made easier to explore and to use. We present in this study an objective comparison of five DR methods, by evaluating their capacity to provide a usable input to the K-means clustering algorithm. We also suggest a method to automatically find a suitable number of classes K, using objective "cluster validity indexes" over a range of values for K. Ten Landsat images have been processed, yielding a classification rate in the 70-80% range. Our results also show that classical linear methods, though slightly outperformed by more recent nonlinear al…
Integration of high and low resolution NDVI data for monitoring vegetation in Mediterranean environments
1998
Abstract The integration of the useful features of high and low spatial and temporal resolution satellite data is a major issue in remote sensing studies. The current work presents the development and testing of a procedure based on classification and regression analysis techniques for generating an NDVI data set with the spatial resolution of Landsat TM images and the temporal resolution of NOAA AVHRR maximum-value composites. The procedure begins with a classification of the high resolution TM data which yields land use references. These are degraded to low spatial resolution in order to produce abundance images comparable with the AVHRR data. Linear regressions are then applied between t…
Optical calibration of a multispectral imaging system based on interference filters
2005
We present a new approach to optically calibrate a multispectral imaging system based on interference filters. Such a system typically suffers from some blurring of its channel images. Because the effectiveness of spectrum reconstruction depends heavily on the quality of the acquired channel images, and because this blurring negatively affects them, a method for deblurring and denoising them is required. The blur is modeled as a uniform intensity distribution within a circular disk. It allows us to characterize, quantitatively, the degradation for each channel image. In terms of global reduction of the blur, it consists of the choice of the best channel for the focus adjustment according to…
Optimal extension of multispectral image demosaicking algorithms for setting up a one-shot camera video acquisition system
2022
Multispectral images are acquired using multispectral cameras equipped with CCD or CMOS sensors which sample the visible or near infrared spectrum according to specific spectral bands. A mosaic of multispectral MSFA filters is superimposed on the surface of the sensors to acquire a raw image called an MSFA image. In the MSFA image, only one spectral band is available per pixel, the demosaicking process is necessary to estimate the multispectral image at full spatio-spectral resolution. Motivated by the success of single-sensor cameras capturing the image in a single exposure that use CFA filters, we performed a comparative study of a few recent color image demosaicking algorithms and experi…
Multispectral imaging and its use for face recognition : sensory data enhancement
2015
In this thesis, we focus on multispectral image for face recognition. With such application,the quality of the image is an important factor that affects the accuracy of therecognition. However, the sensory data are in general corrupted by noise. Thus, wepropose several denoising algorithms that are able to ensure a good tradeoff betweennoise removal and details preservation. Furthermore, characterizing regions and detailsof the face can improve recognition. We focus also in this thesis on multispectral imagesegmentation particularly clustering techniques and cluster analysis. The effectiveness ofthe proposed algorithms is illustrated by comparing them with state-of-the-art methodsusing both…