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Convolutional Neural Network (CNN) has attracted much attention for feature learning and image classification, mostly related to close range photography. As a benchmark work, we trained a relatively large CNN to classify SAR image patches into five different categories, where the image patches tiled and annotated from a typical TerraSAR-X spotlight scene of Wuhan, China. The neural network designed...
Classification has been among the central issues of hyperspectral application. However, due to the well-known Hughes phenomenon, most of the methods suffer from the curse of dimensionality and deeply rely on traditional dimensional reduction like Principle Component Analysis (PCA). In this paper, combining spatial and spectral information jointly, we propose a novel deep classification framework....
Analysis of satellite images plays an increasingly vital role in environment and climate monitoring, especially in detecting and managing natural disaster. In this paper, we proposed an automatic disaster detection system by implementing one of the advance deep learning techniques, convolutional neural network (CNN), to analysis satellite images. The neural network consists of 3 convolutional layers,...
In this paper, we introduced a deep learning model: Convolutional neural network(CNN) from the field of natural image classification and restoration, to solve general quality improving tasks for remote sensing images, including super-resolution, denoising and haze removal. To take advantage of the content similarity among aerial images and the learning ability of deep learning models, we proposed...
To combat the well-known Hughes phenomenon occurred in hyperspectral classification, most of the previous works adopt dimensionality reduction or manifold learning technique before supervised learning. While in this paper, we propose a different scheme: First, we design a pixel-wise classifier based on Convolutional Neural Network that could directly mapping observed spectrum to class distribution...
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