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Extraction of objects such as buildings from very high resolution(VHR) remote sensing imagery is an important task nowadays. In practical application, the extraction precision is normally satisfied through interactive manual input. Therefore, we propose a semi-automatic building extraction framework with an energy minimization model, which includes two stages: the first stage generates the coarse...
Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual...
An automatic approach to detect bilge dumping in synthetic aperture radar (SAR) images over Southern African oceans is proposed. The approach uses a threshold-based algorithm and a region-based active contour model (ACM) algorithm to achieve an efficient bilge dump detection tool. A threshold method was used to detect areas with a high bilge dump probability while the ACM method is used to get closed...
This paper has proposed a new method that integrates the advantage of optical image for delineating land surface boundaries and the superiority of PolSAR data for obtaining corn information despite bad weather conditions. The comparison between the proposed method and both pixel- and object-based method was made to test their performance for corn classification. The analysis shows that the proposed...
This letter depicts a ship detection scheme for synthetic aperture radar images, utilizing a segmentation based global iterative censoring algorithm. In the proposed scheme, the fuzzy local information c-means clustering (RFLICM) algorithm is adopted to partition the inhomogeneous SAR image into numerous homogeneous sub-regions, thereby eliminating the performance degradation caused by SAR image inhomogeneity...
Traditional approaches to structured semantic segmentation employ appearance-based classifiers to provide a class-likelihood at each spatial location and then post-process it with Markov Random Fields (MRF) to enforce label smoothness and structure in the output space. The spatial support for such techniques is usually a patch of pixels, which makes the prediction over-smoothed because the borders...
Semantic segmentation is an emerging field in the computer vision community where one can segment and label an object all at once. In this paper, we propose a semantic segmentation algorithm that takes into account both the hyperspectral images and the LiDAR data. In our segmentation framework, we propose a new energy function that is composed of two terms: a unary energy term and a pairwise energy...
Image visualization techniques are mostly based on three bands as RGB color composite channels for human eye to characterize the scene. This, however, is not effective in case of hyper-spectral images (HSI) because they contain dozens of informative spectral bands. To eliminate redundancy of spectral information among these bands, dimensionality reduction (DR) is applied while at the same trying to...
The parcel-based changed detection by adopting the holistic feature can extract the changed parcels in land-use maps[1]. This method of parcel-based change detection uses the holistic feature, which represents each land use parcels clipped by polygons in the land use map with the energy spectrum of WFT and extracts the changed parcels according to the distance threshold between feature vectors associated...
Hyperspectral images in the thermal infrared range are attracting increasing attention in the remote sensing field. Nonetheless, the generation of land cover maps using this innovative kind of remote sensing data has been scarcely studied so far. The aim of this article is to experimentally investigate the potential of various supervised classification approaches to land cover mapping from high spatial...
The objective of this study was to improve individual tree crown delineation by fully exploiting the crown information exhibited in multi-wavelength LiDAR data. The data used in this study were obtained by an Optech's Titan instrument with three wavelengths: 532 nm, 1064 nm, and 1550 nm. Methods were developed to employ both spectral and structural information of tree crowns to separate crowns from...
COSMO-SkyMed (Constellation of Small satellites for Mediterranean basin Observation) is a fundamental, powerful asset to Earth Observation field, in which Italy plays a crucial role at world level. It is an Earth Observation Dual Use System (civil and military) conceived to fulfill both civilian and defense needs, enhancing international partnerships through its Interoperability, Expandability and...
In this paper, a novel generic framework has been designed, developed and validated for addressing simultaneously the tasks of image registration, segmentation and change detection from multisensor, multiresolution, multitemporal satellite image pairs. Our approach models the inter-dependencies of variables through a higher order graph. The proposed formulation is modular with respect to the nature...
This paper presents a new method for polarimetric synthetic aperture radar (PolSAR) image classification. Firstly, to get a reasonable edge strength map, polarimetric information is used in edge strength calculation, and watershed algorithm is used to obtain the oversegmentation using the edge strength. Secondly, a searching table is used to determine the most suitable region to be merged. Finally,...
Image segmentation as a main applying field in parallel computing with high performance, its time complexity and real-time requirements of algorithm needs to continue to improve computer hardware technology and parallel computing algorithm. Mean Shift algorithm is relatively classical in image segmentation fields, which needs no prior knowledge in the process and is an unsupervised segmentation process,...
Nowadays, the accidents of oil spill become more and more frequent, causing pollution to the natural resources, marine environment and lives in the sea. As a result, the detection of oil spill draws more and more attentions. One of the most popular region-based active contour models proposed by Chan and Vese, is widely used to image segmentation. But it can't segment hyperspectral oil spill image...
Navigation landmark features such as docks are often used in ships for localization and searching for shore targets during the voyage, which are of great economic and military significance. Continuous and complete dock data could hardly be extracted by existing coast dock extraction method, because the spatial relationships and other characteristics of dock are often ignored which only considers grayscale...
With the increasement of spatial resolution of remote sensing, the ship detection methods for low-resolution images are no longer suitable. In this study, a ship target automatic detection method for high-resolution remote sensing is proposed, which mainly contains steps of Otsu binary segmentation, morphological operation, calculation of target features and target judgment. The results show that...
The inventory of economic forest planting on mountainous area is of great interest for the shareholders, ecologists, and governors. This study presents a novel object-based remote sensing image texture extraction method to aid the classification of mountain economic forest. Whereas the texture pattern of man-planted forest on mountainous area are similar with human fingerprint on remote sensing images,...
Change Vector Analysis (CVA) is an important change detection method for remote sensing imagery with medium and low resolution. Traditional CVA is a pixel-based method, which is insufficient for high-resolution (HR) imagery. An object-oriented change vector analysis method (OCVA) is proposed in the paper. Image segmentation method was used to get image objects. The object histogtam was extracted as...
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