The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Extracting and identifying objects in very high resolution imagery has been a popular research topic in remote sensing. Since the beginning of this decade, deep learning techniques have revolutionized computer vision providing significant performance gains compared to traditional “shallow” techniques in various challenging vision problems. The training of deep neural networks usually requires very...
Performance of hyperspectral image classification depends on feature extraction. Compared with conventional hand-crafted feature extraction, deep learning can learn feature with more discriminative information. In this paper, a two-channel deep convolutional neural network (Two-CNN) is proposed to learn jointly spectral-spatial feature from hyperspectral image. The proposed model is composed of two...
We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional layer upsamples the data back to the initial resolution,...
Super-resolution techniques use subpixel information to predict high resolution classification maps from coarse images. This study investigates for an unsupervised super-resolution approach which considers the image features to predict target spatial dependencies. Novelty of the approach is that the convolution neural networks and deep autoencoders are explored in this context. Evaluation over standard...
This paper proposes a novel unsupervised change detection model at pixel level based on Pulse-Coupled Neural Networks (PCNN) combined with Change Vector Analysis (CVA). The proposed algorithm has the following steps: (a) computation of Difference Image (DI) corresponding to the magnitudes of Spectral Change Vectors (SCV); (b) evaluation of the total number of firings of each PCNN neuron for a given...
China is currently suffering from a heavy PM2.5 pollution. To estimate ground-level PM2.5 from satellite-observed aerosol optical depth (AOD), many regional studies have been undertaken, but a few at national scale in China. Moreover, due to the wide spatial range and complex meteorological fields, the previous models' estimation accuracy of PM2.5 still has space to improve. In this paper, using the...
In this paper, we describe a novel deep convolutional neural networks (CNN) based approach called contextual deep CNN that can jointly exploit spatial and spectral features for hyperspectral image classification. The contextual deep CNN first concurrently applies multiple 3-dimensional local convolutional filters with different sizes jointly exploiting spatial and spectral features of a hyperspectral...
In this paper, convolutional neural networks (CNNs) is employed for remote-sensing scene classification, which fully utilizes the semantic features extracted from the images while ignoring some traditional features. Consider the limited labeled samples, CaffeNet model as the pre-trained architecture is adopted. By fine-tuning the pre-trained models, the proposed method is expected to be robust and...
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....
Filtering process plays significant roles in the generation of a digital elevation model (DEM) from interferogram obtained by interferometric synthetic aperture radar (InSAR). In order to remove the distortion of a so-called singular unit (SU), this paper proposes two novel filtering techniques which both exhibit strong nonlinearity. The first method attempts to remove the distortion by focusing on...
Convolutional neural network (CNN) has outstanding performance on nature image classification, such as facial recognition, ImageNet Large Scale Visual Recognition Challenge. However, due to scale variation of the same object in scene, it's difficult to directly utilize CNN for remote sensing image classification. In order to solve this problem, scene classification based on a random-scale stretched...
SAR images from Italian COSMO-SkyMed mission can have a significant impact on the production and updates of land cover maps. However, for the full exploitation of the data and their application to nationwide extensions, robust automatic procedures need to be designed. In this paper we present the preliminary results obtained by the implementation of a processing scheme using COSMO-SkyMed images to...
A ground-based optical telescope equipped with CCD sensor has become an important tool for space debris monitoring in order to maintain a space debris catalogues. In this paper, we emphasized on an enhanced technique for a small and dimming space object extraction. Traditionally, the static background subtraction based on median image technique is widely used to extract the moving space object in...
The accurate assessment of chlorophyll-a concentration in turbid coastal waters by means of remote sensing is quite challenging, due to the optical complexity of these waters. In this study, a semi-analytical approach is used to analyze the mathematical relationship between chlorophyll-a concentration and remote sensing reflectance, then neural network technology is proposed to simulate the mathematical...
Urban functional zones refer to areas (or regions) of a city which provide specific urban functions for peoples who lived in the city. The spatial layout of buildings in functional zone show a specific pattern, e.g. residual areas usually have similar builds and the positions of which are highly organized. In this paper, we show that it is possible to identify urban functional zones from a remote...
At present, the performance of image registration mainly depends on the extracted features in feature-based image registration. However, due to the speckle noise, synthetic aperture radar (SAR) image registration will have a lower accuracy and less robustness. For this purpose, we design a deep neural network (DNN) for SAR image registration, using the DNN to learn the image features, automatically...
Hyperspectral image (HIS) classification is a hot topic in remote sensing community and most of the existing methods extract the features of original Hyperspectral data using shallow layer networks such as neural network (NN) and support vector machine (SVM). As deep learning recently achieves great success in machine learning and pattern recognition area for its ability in deep feature extraction...
In this paper, we joint autoencoder with active learning for hyperspectral imagery classification. Specifically, we learn the classifier via autoencoder, where the most informative samples are acitvely selected through the interaction between the autoencoder and active learning. Experimental results, conducted using both the Kennedy Space Center and the Indian Pines hyperspectral images, show that...
This paper proposes a method that uses both spectral and spatial information to segment remote sensing hyperspectral images. After a hyperspectral image is over-segmented into superpixels, a deep Convolutional Neural Network (CNN) is used to perform superpixel-level labelling. To further delineate objects from a hyperspectral scene, this paper attempts to combine the properties of CNN and Conditional...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.