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Remote sensing image registration is still a challenging task because of diverse image types and the lack of a consistent transformation. To improve image registration in remote sensing, this paper develops a robust and accurate feature point matching framework. A modified scale-invariant feature transform (SIFT) method is first introduced for feature detection and pair matching. Based on the properties...
A novel composite approach through integration of variational optical flow and surface splines is presented to obtain sub-pixel accurate dense disparity map for remotely sensed stereo image pair. It is well known that, surface splines handle geometric distortion very well. The performance of surface splines for dense correspondence can be significantly improved by the reliable control points, but...
For remote sensing image understanding, target detection is one of the most important tasks. In this paper, we propose one object detection method based on region proposal detection via active contour model and detection based on one-class classification method. The large scale remote sensing image is split into several connected components. And then, the proposed algorithm detects the object from...
Geographic mapping of coffee crops by using remote sensing images and supervised classification has been a challenging research subject. Besides the intrinsic problems caused by the nature of multi-spectral information, coffee crops are non-seasonal and usually planted in mountains, which requires encoding and learning a huge diversity of patterns during the classifier training. In this paper, we...
Remote Sensing (RS) data have been increasingly applied to assess agricultural yield, production and crop condition. In tropical areas, crop dynamics are complex due to multiple agricultural practices such as irrigation, non-tillage, crop rotation and multiple harvest per year. Spatial and temporal information can improve the performance in land-cover and crop type classification tasks. In this context...
The Earth observation satellites have been monitoring the earth's surface for a long time, and the images taken by the satellites contain large amounts of valuable data. However, it is extremely hard work to manually analyze such huge data. Thus, a method of automatic object detection is needed for satellite images to facilitate efficient data analyses. This paper describes a new image feature extended...
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-toend trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output...
Inshore ship detection in remote sensing images is a challenging task because of the connectivity and similarity between ships and backgrounds. The usual shape feature is not always applicable because sometimes it is hard to be extracted. In this paper, deep features extracted from a convolutional neural network (CNN) are used for inshore ship detection. In order to feed the CNN with exclusively positive...
Natural disasters such as earthquakes and tsunamis often have a devastating effect on human life and cause noticeable damage to infrastructure. Active research has been ongoing to mitigate the impact of these catastrophes and preclude the economic losses. The existing methods that utilize pre-event and post-event images not only require the immediate and guaranteed availability of the appropriate...
SAR (Synthetic aperture radar) has the advantages that it can be observed all day long and all weather. Space borne SAR is able to observe the target area periodically, therefore the extraction of lakes using SAR image is more and more widely used. The detection and update of lake plays an important role in water resources managing, water environment monitoring and watershed management. The traditional...
In view of textual remote sensing image classification, a classification approach based on Extreme Learning Machine (ELM) in introduced. As the performance of ELM is mainly affected by the value of input weights and hidden biases genetic algorithm (GA) and particle swarm optimization algorithm (PSO) have been used to learn these parameters for ELM in order to improve the stability of extreme learning...
Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. Remote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously time of computation. At...
It is a hotspot in the field of remote sensing image analysis and application by using the macro and real-time features of the remote sensing image data for its change in Land and Resources. This paper introduces an automatic change monitoring method with remote sensing image data and historical interpretation vector data, which is based on the Gaussian Mixture Model and the vector-guided image spot...
Advancement in the recent technology affects a wide research in the field of Image Fusion. It is the more researched challenges in Computer vision, Remote sensing, Medical Imaging, and Target Recognition. The idea behind the image fusion is merging complementary and redundant information from multiple images in such a way, as to retain the most desirable characteristics of every image. The single...
In this paper, we propose a post classification smoothing method aimed at improving the accuracy and visual appearance of sub-decimeter image classification results. Starting from the class confidence maps of a supervised classifier, we find a set of high confidence markers and propagate labels on an extended region adjacency graph. We apply the proposed method on a challenging 5cm resolution dataset...
In this work, a diversified deep structural metric learning is proposed for remote sensing image classification. Firstly, a deep structural metric learning is introduced to take full advantage of structural information of training batches. Secondly, we impose a diversity regularization over the factors of deep structural metric learning to encourage them to be uncorrelated, such that each factor tends...
Building extraction from remote sensing images is a longstanding topic in land use analysis and applications of remote sensing. Variations in shape and appearance of buildings, occlusions and other unpredictable factors increase the hardness of automatic building extraction. Numerous methods have been proposed during the last several decays, but most of these works are task oriented and lack of generalization...
Morphological attribute profiles (MAPs) are one of the most effective methodologies to characterize the spatial information in remote sensing images. This technique extracts components able to accurately describe objects in the surface of the Earth. In this work, we present a new method for impervious surface extraction from multispectral images using morphological attribute profiles. The proposed...
Self-Dual Attribute Profiles (SDAPs) have proven to be an effective method for extracting spatial features able to improve scene classification of remote sensing images with very high spatial resolution. An SDAP is a multilevel decomposition of an image obtained with a sequence of transformations performed by attribute filters over the Tree of Shapes (ToS). One of the main issues with this technique...
Semantic segmentation for remote sensing images is a critical process in the workflow of object-based image analysis. Recently, convolutional neural networks(CNNs) are powerful visual models that yield hierarchies of features. In this paper, we propose a deep convolutional encoder-decoder model for remote sensing images segmentation. Specifically, we rely on the encoder network to extract the high-level...
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