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Aim at the ill-posedness of vegetation biophysical variables inversion problems, the paper presents a multi-scale, multistage (MSMS) inversion approach based on field data, multi-resolution remotely sensed observations and spatial knowledge for estimating crop leaf area index (LAI). The proposed MSMS inversion method takes advantage of multiple stages inversion strategy and prior information. Firstly,...
The aim of this study is to propose and evaluate methodology for identification and delineation of individual tree crowns, using Lidar and multispectral data fusion. Methods implementing data fusion are based on image binarization (thresholding) and region growing segmentation algorithm. Results were compared with template matching method for multi-spectral data and region growing algorithm using...
The urbanization process changed the urban ecological land and consequently affected the quality of urban residents' environment, and it was very important to obtain urban ecological land cover information. In this paper, an object-oriented method was proposed to extract urban ecological land cover from the multiple-channel images acquired by Chinese Gaofen-1 (GF-1) satellite. Taking Beijing City...
Infrared spectrums play an important role in the information extraction of rock and minerals. Spectrum simulation is a fundamental issue of land surface scene simulation and image simulation of remote sensing systems. Signal to Noise Ratio is regarded as an essential parameter of instrument and remote sensing image. In this study, we used MODTRAN to simulate apparent radiance and different levels...
Each year, numerous disasters cause high amount of human and material losses. With the presence of both technological and social means like very high spatial resolution (VHR) satellite images and the International Charter “Space and Major Disasters” respectively, decision makers can obtain the needed information to make fast life-saving decisions. The automation of parts of the image analysis process...
This study addresses the detection of the land degradation in the arid and semi-arid areas using remote sensing techniques. Using free data like Landsat and Shuttle Radar Topography Mission (SRTM) DEM, we mapped land degradation and its factors in Western Australia. By combining vegetation index estimated from Landsat data and geographic information calculated from SRTM DEM, the land situation was...
In this paper, we propose a novel deep convex network method for domain adaptation in multitemporal remote sensing imagery. We fuse the capabilities of the extreme learning machine (ELM) classifier and local feature descriptor techniques to boost the classification accuracy. We use the Affine Scale Invariant Feature Transform (ASIFT) to extract the key points from the image pair, i.e. source and target...
We present a method based on Markov Random Fields (MRFs) for conducting decision level fusion of segments derived from multiple images of the same region. These images are not required to share the same resolution or sensor characteristics. By working at the segment level we preserve the advantages of segment based image classification while also incorporating the benefits of using multiple image...
Urban impervious surface mapping using moderate-resolution optical images such as Landsat images could be challenging due to the complexity of urban land cover. The study aims to combine optical and PolSAR images to improve accuracy of impervious surface classification. A scene of Landsat-5 TM image and a scene of RADARSAT-2 full-polarized imagery of Kitchener-Waterloo were used. The classification...
In this paper we propose a new methodology to automatically generate retrospective high resolution land cover maps on a regular basis for the whole territory of Ukraine. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is used for multi-temporal Landsat-4/5/7 satellites imagery classification with previously restored missing data due to clouds, shadows and non-regular coverage...
Remote Predictive Mapping can help identifying potential sites for mineral exploration. Elevation information is necessary for this purpose, but current freely available elevation data north of the 60th parallel needs improvement. This paper presents a method for creating stereoradargrammetric digital surface models (DSM) using Radarsat-2 (RS-2) images. The study site is a 23 000 km2 area located...
SAR interferometry is a well-known technique to produce digital surface models (DSM). In the global scale, the quality of these models significantly increased with the use of bistatic systems as for the TanDEM-X mission. DSM uncertainty can be measured in two scales: a global and a local one. A global scale analysis involves measures for the complete take, characterizing the whole correctness with...
This paper presents a new method to detect damaged buildings caused by earthquake from high spatial resolution remote sensing image. We found that the probability of multiple gradient orientations is greater in a local area within a damaged building than that in a local area within an intact building. Therefore, a new feature (Local Gradient Orientation Entropy, LGOE) was put forward to determine...
The present paper aims to review the role satellite remote sensing played during the response phase to the largest (in terms of mortality) natural disaster occurred in 2013, i.e. the tropical typhoon Haiyan that struck the Philippines in November 2013. The outcomes of a thorough analysis of the emergency mapping products (about 750 maps) released in the aftermath of the event and in the following...
In the present paper, efficiency and competence of an ensemble method is explored in the context of large number of available spectral information. Classification results of ensemble method are compared with the results generated by a single classifier utilizing all spectral channels. In the present study, an ensemble committee is constructed by distributing spectral channels among five members of...
TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation [1]-[4]. With a typical separation between the satellites of 120 to 500 m a global Digital Elevation Model (DEM) with 2 m relative height accuracy at 12 m posting is being generated. While the...
Kernel-based image classification methods rely on the considered kernel functions that can be chosen with respect to prior information on the adopted features. In remote sensing, histogram features have recently gained an increasing interest due to their capability to address several critical classification problems (e.g., the problem of curse of dimensionality) when appropriate kernels and classifiers...
This paper introduces archetypal dictionaries for a self-taught learning framework for the application of landcover classification. Self-taught learning, an unsupervised representation learning method, is exploited to learn low-dimensional and discriminative higher-level features, which are used as input into a classification algorithm. Experiments are conducted using a multi-spectral Landsat 5 TM...
This paper addresses the problem of parameter optimization for Markov random field (MRF) models for supervised classification of remote sensing images. MRF model parameters generally impact on classification accuracy, and their automatic optimization is still an open issue especially in the supervised case. The proposed approach combines a mean square error (MSE) formulation with Platt's sequential...
Only a small percentage of SAR data are immediately used after the acquisition, the remaining part is archived for a future use. Those data could potentially contain unexploited valuable environmental information. In this paper it is explored the possibility to use quick look (QL) images, generated for cataloguing purposes also to provide information on changes occurred on the scene. In the proposed...
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