Data fusion of remotely sensed images using the wavelet transform : the ARSIS solution

ISSN 0277-786XInternational audienceEarth Observation satellites are often considered to deliver a high spatial resolution image and a set of high spectral resolution images with a lower spatial resolution. Users often want to take advantage of both the high spatial and high spectral resolutions. The ARSIS concept was specially designed to fulfill this requirement and to produce high spectral resolution images with the best spatial resolution available in the set of images with respect to the original spectral content. This concept is based on the wavelet transform and the multiresolution analysis. In this paper, the concept is presented and the different parameters are discussed. Examples of application of ARSIS to real case- studies are provided. Perspectives of use of such a concept are proposed and the benefits to user discussed

Change Detection from Remotely Sensed Images Based on Stationary Wavelet Transform

The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (Kc). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences

Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation

International audienceIn various applications of remote sensing, when high spatial resolution is required in addition with classification results, sensor fusion is a solution. From a set of images with different spatial and spectral resolutions, the aim is to synthesize images with the highest spatial resolution available in the set and with an appropriate spectral content. Several sensor fusion methods exist; most of them improve the spatial resolution but with a poor quality of the spectral content of the resulting image. Based on a multiresolution modeling of the information, the ARSIS concept (from its French name "Amélioration de la Résolution Spatiale par Injection de Structures") was designed in the aim of improving the spatial resolution together with a high-quality in the spectral content of the synthesized images. The general case of application of this concept is described. A quantitative comparison of all presented methods is achieved for a SPOT image. Another example of the fusion of SPOT XS (20 m) and KVR-1000 (2 m) images is given. Practical information for the implementation of the wavelet transform, the multiresolution analysis, and the ARSIS concept by practitioners is given with particular relevance to SPOT and Landsat imagery

Incorporating Multiresolution Analysis With Multiclassifiers And Decision Fusion For Hyperspectral Remote Sensing

The ongoing development and increased affordability of hyperspectral sensors are increasing their utilization in a variety of applications, such as agricultural monitoring and decision making. Hyperspectral Automated Target Recognition (ATR) systems typically rely heavily on dimensionality reduction methods, and particularly intelligent reduction methods referred to as feature extraction techniques. This dissertation reports on the development, implementation, and testing of new hyperspectral analysis techniques for ATR systems, including their use in agricultural applications where ground truthed observations available for training the ATR system are typically very limited. This dissertation reports the design of effective methods for grouping and down-selecting Discrete Wavelet Transform (DWT) coefficients and the design of automated Wavelet Packet Decomposition (WPD) filter tree pruning methods for use within the framework of a Multiclassifiers and Decision Fusion (MCDF) ATR system. The efficacy of the DWT MCDF and WPD MCDF systems are compared to existing ATR methods commonly used in hyperspectral remote sensing applications. The newly developed methods’ sensitivity to operating conditions, such as mother wavelet selection, decomposition level, and quantity and quality of available training data are also investigated. The newly developed ATR systems are applied to the problem of hyperspectral remote sensing of agricultural food crop contaminations either by airborne chemical application, specifically Glufosinate herbicide at varying concentrations applied to corn crops, or by biological infestation, specifically soybean rust disease in soybean crops. The DWT MCDF and WPD MCDF methods significantly outperform conventional hyperspectral ATR methods. For example, when detecting and classifying varying levels of soybean rust infestation, stepwise linear discriminant analysis, results in accuracies of approximately 30%-40%, but WPD MCDF methods result in accuracies of approximately 70%-80%