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Mapping activities of urban land change is important for human activity to Earth's dynamic change. To get the detailed information on urban development maps in large area, dense training samples are needed in different area and specific season, which is cost-consuming. To overcome this issue, we provide a transfer learning method based on deep information to extract urban areas in all season and different...
Automatic and accurate detection of man-made objects, such as buildings, is one of the main problems that the remote sensing community has been focusing on for the last decades. In this paper, we propose a Conditional Random Field (CRF) formulation which is using edge/boundary localization priors towards accurate building detection. These edge priors have been integrated/fused with the classification...
Establishing up-to-date nationwide building maps is essential to understand urban dynamics, such as estimating population and urban planning and many other applications. However, an efficient and effective solution is yet to be developed. In this paper, for the first time we evaluate three state-of-the-art CNNs for detecting buildings across entire United States using aerial images. The three CNN...
The geometrical characteristic and low-level manually designed features are usually used to detect airports in optical remote sensing images. But it is insufficient to describe airport in low resolution and illumination environment. This paper presents a hard example mining algorithm to train the end-to-end deep convolutional neural network for airport detection in complex situation. Compared with...
Ship category recognition is one of the remote sensing applications that requires designing accurate image representation and classification models. Training these models is usually a data hungry process, that requires a lot of labeled data which are usually scarce and expensive. As unlabeled data are more abundant and relatively cheaper, transductive methods exploiting these data are highly preferred...
Classification of multisensor data provides potential advantages over a single sensor in accuracy. In this paper, deep bimodal autoencoders are proposed for classification of fusing synthetic aperture radar (SAR) and multispectral images. The proposed deep network based on autoencoders is trained to discover both independencies of each modality and correlations across the modalities. Specifically,...
This paper proposed a deep convolutional neural network (DCNN) based framework for large-scale oil palm tree detection using high-resolution remote sensing images in Malaysia. Different from the previous palm tree or tree crown detection studies, the palm trees in our study area are very crowded and their crowns often overlap. Moreover, there are various land cover types in our study area, e.g. impervious,...
The classification procedure to identify remote sensing signatures from a particular geographical region can be achieved using an accurate identification model that is based on multispectral data and uses pixel statistics for the class description. This methodology is referred to as the Multispectral Identification Model. This paper presents this particular methodology applied to large remote sensing...
Using monthly as well as annual statistics, we investigate the potentials of synergetic utilization of multispectral and C-band SAR data for the classification of a study site in the central Brazilian state of Mato Grosso. We aim at the classification of five tropical land cover classes (primary forests, secondary vegetation, pasture, agricultural, water), and highlight the potentials of standalone...
The likelihood of transitions between pairs of land cover and land use classes in a given time interval and environmental context can be used to impose classification restrictions on an image or to evaluate results. This study presents a methodology for using the likelihood of transitions between classes to improve land cover classification, given a base map (a supposedly accurate map for the same...
This paper proposes a Genetic Algorithm (GA) approach to clean a given classifier training set for remote sensing image analysis. Starting from an initial set of training data, the new method called GA-Training Label Purifying (GA-TLP) consists of the significant training sample selection using GAs in order to maximize the classifier accuracy. This means to retain the most informative samples and...
In this paper, we proposed a new semi-supervised method for polarimetric synthetic aperture radar (PolSAR) terrain classification based on improved tri-training. This method only needs a few numbers of labeled samples to achieve the results obtained by traditional supervised classification methods. First, it uses a variety of target decomposition methods to obtain high-dimensional feature. Second,...
This paper developed an approach to determine optimal parameters, C and s, for support vector domain description (SVDD) model to map specific land cover from integrating of training and window-based validation sets (WVS-SVDD). The validation set based on window-based approach made a tighten hypersphere because of compact constraint by the outlier pixels which were located closely to the target class...
Artificial neural networks (ANNs) have been widely used in the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention. Unlike traditional CNNs methods, where the relevant information to classify the elements of a remotely sensed image is extracted only from the last fully-connected layer, the new adaptive deep pyramid matching (ADPM)...
There are many attempts that utilize deep learning methods to solve the problem of classification in remote sensing images. Convolutional Neural Networks (CNN) have made very good performance for various visual tasks, and marked their important place in all deep learning models. However, for some classification tasks of remote sensing images, CNN could not demonstrate their full potential because...
Fusing different sensors with different data modalities is a common technique to improve land cover classification performance in remote sensing. However, all modalities are rarely available for all test data, and this missing data problem poses severe challenges for multi-modal learning. Inspired by recent successes in deep learning, we propose as a remedy a convolutional neural network architecture...
One significant advantage of the deep convolutional neural networks (DCNN) is their representational ability for local complex structures. Inspired by this observation, a DCNN based residual learning model is proposed to learn a nonlinear mapping function between the high-resolution (HR) and low-resolution (LR) image patches. The DCNN is trained based on image patches, which are only sampled from...
In this work, we consider the problem of detecting target objects in remote sensing imagery; such as detecting rooftops, trees, or cars in color/hyperspectral imagery. Many detection algorithms for this problem work by assigning a decision statistic (or “confidence”) to all, or a subset, of spatial locations in the data. A threshold is then applied to the statistics to identify detections. The detection...
This paper presents an approach to the update of land-cover maps by classifying Remote Sensing (RS) images in an unsupervised way. The proposed method assumes that: i) an old thematic map is available; ii) no ground truth data are available; iii) the source used to generate the available thematic map is unknown. To classify the most recent RS image available on the considered area, the method automatically...
This paper presents a new methodology for classification of local climate zones based on ensemble learning techniques. Landsat-8 data and open street map data are used to extract spectral-spatial features, including spectral reflectance, spectral indexes, and morphological profiles fed to subsequent classification methods as inputs. Canonical correlation forests and rotation forests are used for the...
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