Lusi is a mud volcano in East Java, Indonesia, which started its eruption on 29thMay 2006 and never stopped. To study this volcano, we used SAR interferometry with ALOS/Palsar satellite images from 2006 to 2010. By creation of a set of interferograms, and suppression of unwanted components, such as Earth curvature and elevation, we were able to compute the motion that occurred between two dates. The obtained set of motions shows that the region of Lusi is undergoing a general subsidence of several metres. However, as this region is very wet, more precise results, on the volcano itself, can be obtained by GPS campaigns.

On September 30th, 2009, a major earthquake of magnitude 7.6 occurred near the west coast of Sumatra close to the city of Padang. On October 1st a significant aftershock (magnitude 6.6) occurred 270 km away. The casualties are estimated at 1200. This earthquake comes at a time when seismic activity in the region is particularly high. The purpose of this paper is to show for an earthquake occurring in such regions how L-band interferometry can contribute to both damage assessment and a better understanding of the underlying geological phenomena.

This paper discusses several ways in which a community of volunteers can be put together to generate good quality geographical information by the use of remote sensing image analysis tools. Three different scenarios are proposed and a system architecture, based on existing open source solutions, is suggested and discussed. The main objective of this paper is to motivate contributions to build such a system.

Mangroves are an important part of the ecosystem in tropical region. Unfortunately, they are also under intense ecological pressure from fishing, tourism or logging. As they are often in not easily accessible places and scattered over large areas, satellite observation is an ideal solution to monitor the mangrove evolution over the past few years. However, the mapping of mangrove from satellite images is a difficult task and mostly done manually. Here we propose a detection method based on support vector machine, exploring more than 100 features, providing a good accurary, enabling the mangrove expert to focus on the most difficult areas.

Baseline calibration is needed in most of SAR interferometry processing. An iterative optimization of baseline with constrain of relative platform position is presented in this paper. The SAR passes which gives inaccurate platform position is successfully detected and calibrated using this algorithm. After processing, new estimated baseline improves the quality of interferogram. Existence of reference Digital Elevation Model (DEM) error and atmospheric phase screen (APS) can also be detected from the convergence value. This method is based on a reversed concept of platform position estimation from interferometric result. Validation of method performed on multiple SAR images over Singapore.

Orfeo Toolbox, OTB, is a remote sensing image processing library developed by CNES, the French Space Agency. OTB is distributed as Open Source software and is therefore available for any remote sensing scientist or processing chain developer. This paper describes the main features of OTB, how it can be used and the expected evolutions in the coming months.

Satellite observation is particularly enticing due to its large acquisition capabilities. However these large capabilities kindle new challenges for information analysis. Object counting is one of those. To help releasing constraints on the human operator, it is important to free him from repetitive tasks and focus his attention on the high level tasks for which algorithms are not suitable yet. This abstract focuses on building counting in dense areas. The processing is done using the Or-feo Toolbox, an open-source image processing library. This paper proposes several methods with different trade-offs in terms of performance and user involvement. The method has been adapted and successfully used in other situations, as for instance counting tree stands or tents in a refugee camp.

High resolution remote sensing image segmentation is a great challenge in terms of potential applications, but also because of the difficulty of the task. Fully automatic algorithms are not able to extract all the desired features from complex images but visual image analysis is time consuming and tedious (therefore error prone). In this work we present a simple, yet powerful approach for interactive image segmentation. This approach combines the best of the automatic image processing together with the ability of a human operator to choose the objects of interest for a given application. Results are presented on a wide variety of objects and contexts.

The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.

This paper deals with the optimization of a new technique of image compression. After the wavelet transform of an image, blocks of coefficients are further linearly decomposed using a basis selected in a dictionary. This dictionary is known by both the encoder and the decoder. This approach is a generalization of the bandelet transform. This paper investigates the problem of the best basis selection. On each block of wavelet coefficients, this selection is made by minimization of a Lagrangian rate-distortion criterion. Theoretical expressions of the optimal Lagrangian multiplier can be computed based on asymptotic hypotheses. A nearly exhaustive search of the optimal Lagrangian multiplier is done for the compression of high resolution satellite images. This numerical study validates the asymptotic theoretical expressions but as well provides a refined expression of the Lagrangian multiplier. At last, the compression results obtained using those different expressions are compared to the optimal compression results obtained with the exhaustive search.

Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity. Some wavelet-based compression algorithms have been successfully used for some hyperspectral space missions. This paper focuses on the optimization of a full wavelet compression system for hyperspectral images. Each step of the compression algorithm is studied and optimized. First, an algorithm to find the optimal 3-D wavelet decomposition in a rate-distortion sense is defined. Then, it is shown that a specific fixed decomposition has almost the same performance, while being more useful in terms of complexity issues. It is shown that this decomposition significantly improves the classical isotropic decomposition. One of the most useful properties of this fixed decomposition is that it allows the use of zero tree algorithms. Various tree structures, creating a relationship between coefficients, are compared. Two efficient compression methods based on zerotree coding (EZW and SPIHT) are adapted on this near-optimal decomposition with the best tree structure found. Performances are compared with the adaptation of JPEG 2000 for hyperspectral images on six different areas presenting different statistical properties.

Zerotree based coders have shown a good ability to be successfully adapted to 3D image coding. This paper focuses on the adaptation of EZW for the compression of hyperspectral images with reduced complexity. The subordinate pass is removed so that the location of significant coefficients does not need to be kept in memory. To compensate the quality loss due to this removal, a signed binary digit representation is used to increase the efficiency of zerotrees. Contextual arithmetic coding with very limited contexts is also used. Finally, we show that this simplified version of 3D-EZW performs almost as well as the original one.

Automatic road extraction is a critical feature for an efficient use of remote sensing imagery in most contexts. This paper proposes a robust geometric method to provide a first step extraction level. These results can be used as an initialization for other algorithms or as a starting point for manual road extraction. Results of the extraction are vectorized for GIS integration and for a better interaction with human experts that can refine the results. The algorithm is fast, has very few parameters and is only slightly affected by the image properties (resolution, noise). The algorithm is available in the open-source Orfeo toolbox.

Hyperspectral sensors have been of a growing interest over the past few decades for Earth observation as well as deep space exploration. However, the amount of data provided by such sensors requires an efficient compression system which is yet to be defined. It is hoped that the particular statistical properties of such images can be used to obtain very efficient compression algorithms. This paper proposes a method to find the most suitable wavelet decomposition for hyperspectral images and introduces the possibility of non isotropic decomposition. The decomposition is made by choosing the decomposition that provides an optimal rate-distortion trade-off. The obtained decomposition exhibits better performances in terms of rate-distortion curves compared to isotropic decomposition for high bitrates as well as for low bitrates

Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient compression techniques are required, and lossy compression, specifically, will have a role to play, provided its impact on remote sensing applications remains insignificant. To assess the data quality, suitable distortion measures relevant to end-user applications are required. Quality criteria are also of a major interest for the conception and development of new sensors to define their requirements and specifications. This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. Some are traditionally used in image and video coding and are adapted here to hyperspectral images. Others are specific to hyperspectral data. We also propose the adaptation of two advanced criteria in the presence of different simulated degradations on AVIRIS hyperspectral images. Finally, five criteria are selected to give an accurate representation of the nature and the level of the degradation affecting hyperspectral data.