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Remote sensing image segmentation is the critical process in the workflow of object-based image analysis. Recently, region merging methods have attracted growing attention because they are able to utilize more features than spectral signals derived from initial segments. However, the existing algorithms commonly use fixed parameters to control the process of region merging, which limits the possibility...
Object-based image analysis (OBIA) provides a better solution for information extraction from high spatial resolution remote sensing image. Currently, selection of scale parameters is often dependent on subjective trial-and-error methods or post-evaluation of multi-segmentation, which directly reduces efficiency of land cover classification. This paper proposes a OBIA classification method combining...
The mean shift algorithm shows a good performance in optical image segmentation. However, conventional mean shift algorithm performs poorly if it is used directly to synthetic aperture radar (SAR) image due to the large dynamic range and strong speckle noise. Recently, a generalized mean shift (GMS) algorithm with an adaptive variable asymmetric bandwidth was proposed for polarimetric SAR (PolSAR)...
Integration of spatial context and spectral features of neighborhood pixels in preprocessing modules prior endmember (EM) extraction algorithms has been recently studied in hyperspectral images processing as a result of their capability in enhancing EMs signatures recognition and computational performance. In this paper, we propose an autonomous preprocessing module using incorporation of a novel...
Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. The technique of remote sensing plays an increasingly important role in the protection and management of coral reef ecosystems. In this paper, an object-based image analysis (OBIA) method was presented to map intra-reef geomorphology of coral...
The history, current status and future program of the Chinese Meteorological Satellite, i.e. Fengyun satellite (FY) is introduced in this presentation. Currently, there are 2 FY polar satellites and 3 FY geostationary satellites are in operational. The type of the instruments amounted on FY satellites includes the optical imager, the atmospheric sounder, the microwave imager, the atmospheric composition...
In this paper, an unsupervised change detection model based on hybrid conditional random field model (HCRF) is proposed for high spatial resolution (HSR) remote sensing imagery. Traditional random field based algorithms are mainly based on the analysis of the difference image which ignores the spatial-temporal change information of ground objects which is important in dealing with HSR imagery. Thus...
Sparse representation-based classification (SRC) assigns a test sample to the class with minimal representation error via a sparse linear combination of all the training samples, which has successfully been applied to hyperspectral imagery (HSI). Meanwhile, spatial information, that means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral...
Radar sensors have received more and more interest for unmanned ground vehicle to sense positive and negative obstacles in unstructured environments or out fields, especially on negative obstacle. In this paper, we present an approach for extracting the features of obstacles from radar images. Based on interferometric synthetic aperture radar (InSAR) images focused by the back-projection (BP) algorithm,...
This study proposed an object-based method to estimate the relationship of optimal thresholds in different The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light (NTL) images for the purpose of mapping large-scale urban dynamics. In the method, the optimal threshold for an urban object was predicted by delineating the reference threshold and relating it...
Aircraft detection is a difficult task in the field of high remote sensing image application. In this paper we present an automatic system for aircraft detection based on locating the candidates of aircrafts and Support Vector Machine (SVM) to detect aircraft from high resolution remote sensing images. The system contains two main modules: in module one, we use segmentation based on statistical region...
The detection of ocean oil spill based on synthetic aperture radar (SAR) image has been a hot topic attracting extensive attention. In this paper, a hybrid scheme, in which we extract feature parameters and then achieve classification as follows, is presented. Two-dimensional (2-D) Otsu algorithm is applied in image segmentation process, and neural network is applied in classification course. Before...
In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we address the issue of segmenting the image into regions to be classified using pre-trained deep neural networks as feature extractors for an SVM-based classifier. An efficient segmentation as a preprocessing step helps learning...
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