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In this paper, we propose an improved road detection method from high-resolution remote sensing images by means of a modified path opening and closing algorithm. To detect high curvature roads, we improved previously used adjacency graphs by enlarging the path angle of modified adjacency graphs. Furthermore, to remove noise efficiently, we combined the length of paths with road geometrical features...
This paper presents a feature guided multi-window area-based matching method for urban remote sensing stereo pairs. The method achieves the goal that producing dense disparity maps for urban remote sensing stereo pairs. The proposed method can be divided into four stages: feature-based matching, edge support region extraction, area-based matching and post-processing. The point feature matching is...
In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are...
In this paper, we propose an algorithm for automatic detection of seals in aerial remote sensing images using features extracted from a pre-trained deep convolutional neural network (CNN). The method consists of three stages: (i) Detection of potential objects, (ii) feature extraction and (iii) classification of potential objects. The first stage is application dependent, with the aim of detecting...
Land-Cover databases (LC-DB) are very useful for environmental purposes, but need to be semantically detailed to provide robust and instructive spatial indicators. Moreover, remote sensed data allow to cover large areas with high temporal resolution. Such multi-temporal data are very useful input to discriminate LC classes. Nevertheless, automatic fusion method need to be developed to provide high...
Land Use/Land Cover (LU/LC) of agricultural areas derived from remotely sensed data still remains very challenging. With regard to the rising availability and the improving spatial resolution of satellite data, multitemporal analyses become increasingly important for remote sensing investigations. Even crops with similar spectral behaviour can be separated by adding spectral information of different...
Mapping tree species is an important issue for forest ecosystem services and habitat assessment. In this study, the ability of Formosat-2 multispectral image time series to discriminate thirteen tree species of temperate woodland is investigated. The discrimination is performed using several learning classifiers and testing three levels of classification. The classification accuracies in terms of...
Image registration is extensively used in many application domains such as medical, remote sensing, computer vision etc. The basic purpose of image registration is to obtain finest geometrical and radio-metrically aligned image from temporal or multi-modal image sensors. In this study, a novel salient feature-based image registration scheme has been designed and implemented by establishing a set of...
Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technology used for estimating displacement of the earth's surface. Phase unwrapping is the most important step in InSAR processing and relies on successful selection of points that appear stable across a set of satellite images taken over time. This paper presents a new algorithm for selecting these points, a problem known as persistent...
This study predicts global forest cover change for the 1980s and 1990s from AVHRR time series metrics in order to show how the series of consistent land cover maps for climate modeling produced by the ESA climate change initiative land cover project can be extended back in time. A Random Forest model was trained on global Landsat derived samples. While the deforestation was underestimated by the model,...
We present detailed reconstructions of variations of seven mountain glaciers situated in the Northern Caucasus: Alibek, Bezengi, Mizhirgi, Kashkatash, Terskol, Ullukam, Tsey. Created using remote sensing data, maps, old photographs and historical descriptions, these highly precise reconstructions cover the period from the end of 19th to the beginning of 20th centuries. For each glacier we identified...
The Japan Aerospace Exploration Agency is generating the global digital elevation/surface model (DEM/DSM) and ortho-rectified image (ORI) using the archived data of the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS, nicknamed “Daichi”), which was operated from 2006 to 2011. The overview and the processing status of the global...
Most of the empirical topographic correction methods are based on the universal assumptions of the relationship between radiations and solar incident angles. The correction accuracy is hardly to be accessed quantitatively. This paper introduces a land cover adaptive C (LCAC) method for topographic correction, and verifies its advantage quantitatively. Experiments on synthetic and real remote sensing...
As a significant digital representation of terrain surface, varieties of DEM products have been available to the public. The most widely used global DEM products are SRTM and ASTER GDEM. Given the comparable horizontal resolution and vertical error, accuracy validation and comparison have been of interest since the release, however, usually on a wide range. In this paper, we presented the results...
In remote sensing, where training data are typically ground-based, mislabeled training data is inevitable. This work handles the mislabeling problem by exploiting the ensemble margin for identifying, then eliminating or correcting the mislabeled training data. The effectiveness of our class noise removal and correction methods is demonstrated in performing mapping of land covers. A comparative analysis...
The quality of the training data used in a supervised image classification can impact on the accuracy of the resulting thematic map obtained. Here the effects of mis-labeled training cases on the accuracy of classifications by discriminant analysis and a support vector machine were explored. The accuracy of both classifiers varied with the amount and nature of mis-labeled training cases. In particular,...
The effectiveness of very high-resolution IKONOS multispectral satellite imagery for land cover/vegetation mapping in Pico da Vara Nature Reserve (S. Miguel Island, Archipelago of the Azores, Portugal) was assessed. A per-pixel supervised classification scheme was developed using two different datasets. The Dataset A included the IKONOS-2 ortho-rectified and atmospherically corrected Visible and Near...
Currently the UAV photographic data is unstable, turbulent and inaccurate, and these flaws make image stitching difficult. This paper proposed an innovative fast method based on global subdivision GeoSOT grid frame. And this method can apply to the high-speed image stitching of a certain area at different times on the UAV. The experiment result shows the advantages of the new method over the traditional...
Approximate spectral clustering (ASC), a recently popular approach for unsupervised land cover identification, applies spectral clustering on a reduced set of data representatives (found by sampling or quantization). ASC enables extraction of clusters with different characteristics by utilizing various information types (such as distance, local density distribution and data topology) for accurate...
Monitoring and mapping greenhouses are important for yield estimation, sustainable crop production, residue management and environmental impact. Conventional approaches based on in situ surveys, which are costly and time consuming, are being replaced by supervised classification of commonly used features extracted from very-high spatial resolution images. Alternatively, we extract (both plastic and...
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