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Land-Cover databases (LC-DB) are mandatory for environmental purposes, but need to be regularly updated to provide robust and instructive spatial indicators. Moreover, an increasing number of sensors, such as optical and SAR satellite images or Lidar point cloud, allow to cover large areas regularly, and with a very high precision. Thus, automatic methods have to be developed to take into account...
With the widely application of high-resolution remote sensing images, its classification has attracted a lot of attention. Most classification methods focus on various combination of features and ignore the similarities between different categories. In this paper we present a modification by combining ScSPM [1] with a dictionary learning method DL-COPAR [2], which separates the particularity and commonality...
A heuristic utilizing both spectral and spatial information is proposed for active learning. It addresses the issue of iteratively querying most informative training samples with a special focus on spatial-contextual image classification. With the aim to utilize all information during the learning process, the proposed heuristic queries unlabeled pixels considering spectral-spatial inconsistency (SSI),...
This paper presents a new paradigm for object based classification of multispectral images. Instead of classifying objects only after the segmentation process is completed, it is proposed to intercept the early stages of the segmentation by iteratively performing classification tests to under growing regions. By applying this simultaneous analysis, mislabeling of objects considered only after segmentation...
Non-randomness within canopies and woody component are two factors limiting the accuracy of indirect leaf area index (LAI) measurement. Here we combine the path length distribution model and Multispectral Canopy Imager (MCI) together for the first time to improve the accuracy. The results show that non-randomness within canopies underestimates 17.1%-28.2% LAI, while woody component overestimates 14...
Road damage detection from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pair of pre-disaster and post-disaster road data for change detection are difficult to obtain due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e. remote sensing imagery or vector map) are...
Cohen's κ coefficient has been widely used for assessing classification results derived from remote sensing data. It however presents several limitations, which are preventing both an efficient use as well as a generalisation of its use. This paper reviews these problems and proposes as an alternative to prefer the Krippendorff's α-coefficient over Cohen's κ. Krippen-dorff's α indeed presents less...
Panchromatic (PAN) satellite imagery comprises only a single band but it has finer resolution in comparison to the multi-spectral band imagery. In the case of feature extraction and classification, although the multi-spectral imagery has an advantage in availability of the different aspect of spectral properties of the ground coverage, the recent studies introducing intelligent classification and...
An important aspect of research in the remote sensing field is to objectively compare different classifiers. This is the foundation of hundreds of research projects and in this paper we will address some raising concerns when evaluating solutions for classification of data sets with skewed class distributions. The quality of assessment is based on the problem specified by the user and the corresponding...
This work investigates the discriminative power of wavelet decomposition based texture features in forest cover classification. Our texture features are used as inputs in a random forests classifier. The performances of this tree-based ensemble classifier are assessed by classification accuracy as well as classification confidence provided by an unsupervised version of ensemble margin. The effectiveness...
This paper addresses the results of the instrument and product performance verification, radiometric and geometric calibration achieved during the since commissioning and routine phase.
In this work, we propose a new multiple morphological component analysis (MMCA) based decomposition framework for remote sensing image classification. The proposed MMCA framework aims at exploiting relevant textural characteristics present in a scene such as content, coarseness, contrast or directionality. Specifically, MMCA decomposes an image into a pair of morphological components (for each textural...
Scene classification is a key problem in the interpretation of high-resolution remote sensing imagery. The state-of-the-art methods, e.g. bag-of-visual-words model and its various extensions as well as the topic models, share similar procedures: patch sampling, feature description/learning and classification. Patch sampling is the first and the key procedure which has a great influence on the results...
The analysis of the seafloor in shallow waters using remote sensing imagery at very high spatial resolution is a very challenging topic due to the minimum signal level received; the presence of noisy contributions from the atmosphere, solar reflection, foam, turbidity and water column; and the limited spectral information available for the classification at such depths that impedes, for example, the...
In this paper a new approach from the combination of band ratioing function and MLP Neural Networks technique is proposed to differentiate between clouds and background in Landsat ETM+ and MSG SEVIRI data. First, in order to increase the contrast of the clouds and background, a band ratioing function is applied to each sub-image. Second, the sub-images are segmented by MLP Neural Networks technique...
The project aerosol-CCI as part of European Space Agency (ESA) Climate Change Initiative (CCI) has provided three aerosol retrieval algorithms for the Advanced Along-Track Scanning Radiometer (AATSR) aboard on ENVISAT. For the purpose of estimating different performance of these three algorithms in Asia, in this paper we compared the Aerosol Optical Depth (AOD) of L2 data (10km×10km) including FMI...
Dual-pol HHVV images acquired by TerraSAR-X/TanDEM-X during 4 years, with 3 different incidence angles, over rice fields in Spain are analysed and employed to retrieve the phe-nological state of rice crops in a scale of 3 to 5 possible intervals. A decision tree approach is employed for the retrieval algorithm. Results show that the joint processing of all angles provides a good performance, only...
This contribution presents two experiments performed with the TerraSAR-X (TSX) and TanDEM-X (TDX) satellites working in the pursuit monostatic configuration. Their objective is to estimate the along-track component of the motion in the scene in repeat-pass scenarios with an accuracy better than the one given by the stripmap azimuth resolution. Such performance is possible by exploiting the angular...
Remote sensing has much to gain from citizen sensing. This is particularly evident in relation to the provision of ground reference data for use in the training and testing stages of supervised image classification analyses used to generate thematic maps from remotely sensed data. Citizens are able to provide data over large geographical areas inexpensively, addressing potential problems connected...
Scene classification for high-resolution remotely sensed imagery have been widely investigated in recent years. However, there is few public, widely accepted and large scale dataset for benchmarking different methods. This paper presents a new and large dataset consisting of 5000 high-resolution remote sensing images which is manually labeled in 20 semantic classes for scene classification. Each class...
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