Learning-based remote sensing
Deep Learning-Based Classification of Remote Sensing Image
... Deep Learning network (DLN), remote sensing image, ...resolution remote sensing image also brought coexistence of the opportunities and challenges to the classification issues, the ...
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Deep learning for urban remote sensing
... A. Deep network architecture We use the SegNet architecture from [3]. SegNet uses an encoder-decoder architecture (cf. Fig. 3). The encoder is based on VGG-16 [6], in which convolutions are followed by a batch ...
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Manifold learning based spectral unmixing of hyperspectral remote sensing data
... The SpecTIR data contain 10 potential endmembers estimated by the HySime algorithm [79], which is eigen decomposition-based and fully automatic, from the air- borne hyperspectral data collected by SpecTIR. The ...
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How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?
... deep learning approaches for computer vision and remote sensing is not an ...in remote sens- ...of remote sensing when it was used to establish state-of-the-art perfor- mances ...
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Remote sensing based estimation of forest biophysical variables using machine learning algorithm
... Using random forest cross validation, the optimum number of variables. were selected[r] ...
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Combining transductive and active learning to improve object-based classification of remote sensing images
... mostly based in context and field survey information instead of the spectral ...optical remote sensing as some of the materials used in civil construction can present spectral signatures close to ...
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A Review of Researches on Deep Learning in Remote Sensing Application
... through remote sensing, in practice the application of remote sensing imagery relies heavily on manual processing, while machine interpretation is only an aid to manual ...of remote ...
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AN E-LEARNING TUTORIAL FOR RADAR REMOTE SENSING WITH RAT
... are themselves divided into several pages illustrating the logical context, forming a hierarchical tree structure. Small tasks must be solved in the process of going through the lessons. These include simple multiple ...
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Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning
... of remote sensing technology has led to the explosive growth of satellite and aerial images in both quantity and ...supervised learning based object detection approaches often require a large ...
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Machine Learning Based Crop Drought Mapping System by UAV Remote Sensing RGB Imagery
... d School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China Water stress has adverse effects on crop growth and yield, where its monitoring plays a vital role in precision crop ...
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An ensemble learning approach for the classification of remote sensing scenes based on covariance pooling of CNN features
... ensemble learning approach based on the concept of covariance pooling of CNN features issued from a pretrained ...deep learning features, we propose an alter- native strategy which employs an ...
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Remote sensing scene classification based on rotation-invariant feature learning and joint decision making
... images, remote sensing scene classification has always been a hot research topic in its related ...of remote sensing datasets including the small scale of scene classes, the lack of rich label ...
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Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning
... quality based on remote sens- ing is demonstrated to be possible and repeatable, it could be useful to identify urban places that show a trend of decreasing quality and take action before they reach ...
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Remote Sensing / An object-based semantic classification method for high resolution remote sensing imagery using ontology
... classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic ...is based on data-driven machine learning, segmentation, ...
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Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
... was based on the object’s size, which means that object based methods, such as CRF or GEOBIA, could increase segmentation performance in future frameworks, especially when the dominant source of error is ...
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LEARNING CLASSIFIERS FOR SCIENCE EVENT DETECTION IN REMOTE SENSING IMAGERY
... The approach to onboard identification of cryosphere events is to classify pixels in the image independently based on the available spectral information. Pixels are classified as belonging to one of five classes: ...
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Ontology-based topological representation of remote-sensing images
... Nagai, Ono, and Shibasaki (2011) presented a method with the stated purpose to supersede human inductive learning and reasoning in complex scene understanding and characterisation by automatically adding ...
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Self-Organizing Map-based Applications in Remote Sensing
... The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the ...
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Remote Sensing Based Piosphere Analysis
... REMOTE SENSING – BASED PIOSPHERE ANALYSIS 137 research be conducted that developed the use of Geographic Information System (GIS) and remote sensing (RS) technologies for monitoring and ...
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Remote Sensing. Vandaag. Voordelen Remote Sensing Wat is Remote Sensing? Vier elementen Remote Sensing systeem
... Remote Sensing is the science or the technique of deriving information about objects at the Earth surface from images using (parts of) the electromagnetic spectrum.. • Measuring electro[r] ...
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