1,721,782 research outputs found
Urban Area Analysis in Single-polarized SAR Images Based On Unsupervised Deep Learning
Full text linkUrban mapping from remote sensing images is important for monitoring urbanization. In this paper, we propose an unsupervised learning algorithm for high-resolution single-polarized synthetic aperture radar (SAR) image to extract man-made targets for urban area analysis. The proposed method mainly focuses on the special physical characteristics of man-made targets that are different from natural areas. Without polarimetric information, we propose the sub-band scattering pattern based on time-frequency analysis to describe the physical properties of targets, and then design an end-to-end neural network to learn the latent features and potential clusters. The proposed method is evaluated on three different urban areas acquired at C-band by Sentinel-1 and Gaofen-3, and X-band by TerraSAR-X, respectively. The experiments present the visualized result of man-made targets extraction and analyze some specific targets to show the effectiveness of our proposed method
Learned Atoms from Multispectral Satellite Image Time Series
No full textWith the on going development of the satellite image time
series (SITS), information retrieval in earth monitoring and
automatic recognition of changes in time series have become important and challenging tasks. The purpose of the present paper is to determine dictionaries of atoms directly learned from the time series images such that a sparse representation of the images is maintained. The filterbanks learned using gradient descent techniques depend on the frequency band at which the satellite’s sensor captures the scene, i.e., for the visible
part of the spectra, the learned filters are close to mean filters over small subregions, whilst, for the infrared part of the spectra, the filters are similar to gradient ones. Furthermore, we aim to express the time changes in SITS in a compact form provided by the newly found sparse representations of the images. The experiments are carried on 4 Landsat images at 30 meters spatial resolution, covering a surface of � 59×51 km2 over the city of Bucharest, Romania, and containing information
both from the visible and from the infrared domains
A Comparative Study of Sample Selection Strategies Based on Optimum Experimental Design for SAR Image Classification
No full textIn this paper, we evaluate sample selection strategies based on optimum experimental design for SAR image classification. Traditionally, support vector machine active learning is widely used by selecting the samples close to the decision surface. Recently, new methods based on optimum experimental design have been developed. To gain a complete understanding of these selection strategies, a comparative study on three approaches, transductive experimental design, manifold adaptive
experimental design and locally linear reconstruction, has
been performed for SAR image classification using different
features. Among the three approaches,we show that manifold adaptive experimental design performs best and stably in terms of both accuracy and computational complexity
Accelerated Knowledge-Driven Image Mining System for Data Fusion in Big Data
No full textIn this paper, we present a knowledge-driven content-based information mining system for data fusion in Big Data. The tool combines, at pixel level, the unsupervised clustering results of different number of features, extracted from different image types, with a user given semantic concepts in order to calculate the posterior probability that allows the final search. The system is able to learn different semantic labels based on Bayesian networks and retrieve the related images with only a few user interactions, greatly optimizing the computational costs and over performing existing similar systems in various
orders of magnitude
Deconvolution Method for Eliminating Reference Signal Coupling/Reflections in Bistatic SAR
Full text linkBistatic radar receivers that use an opportunistic transmitter require a reference channel to capture the original transmitted signal, which is then used as a reference signal for constructing the matched-filter during the range compression step. Because the reference signal is received from line-of-sight, it is orders in magnitude larger than the reflections captured by the receive channel. It is generally difficult to construct the system such that the reference signal is not leaked into the received signal, either via coupling in the circuitry or via reflections off objects in the vicinity of the receiver. Due to its much larger amplitude, the reference signal can easily mask smaller targets with its side-lobes. In this paper we propose a novel deconvolution method for bistatic SAR images as a means of eliminating leakage of the reference signal
Visualization of Image Collection in 3D: Application to Immersive Information Mining
No full textDimensionality reduction for visualization is widely used in
visual data mining where the data is represented by high dimensional features. However, this leads to have an unbalanced and occluded distribution of visual data in display space giving rise to difficulties in browsing images. In this paper, we propose an approach to the visualization of images in a 3D display space in such a way that: (1) images are not occluded and the provided space is used efficiently; (2) similar images are positioned close together. An immersive virtual environment is employed as a 3D display space. Experiments are performed on an optical image dataset represented by color features. A library of dimensionality reduction is employed to reduce the dimensionality to 3D. The results confirm that
the proposed technique can be used in immersive visual data mining for exploring and browsing large-scale datasets
A Case Study for User Evaluation of a CBIR Tool: an Application of Open-Ended Feedback with Comment Clustering and Inductive Categorization
No full textIn the context of Earth Observation (EO), image information
retrieval systems have gained importance as a way to
explore terabytes of archive data. Concurrently, evaluation
of these systems becomes a topic. Evaluation has typically
been conducted in the form of metrics such as Precision
Recall measures, with more recent approaches attempting to include the user in the evaluation process. This paper
presents a more user centered evaluation of a CBIR tool in
an EO context. The evaluation methodology involved open
ended user feedback, which was then inductively
categorized, and its distribution and content were analyzed.
Results are presented, with conclusions indicating certain
aspects of the user experience cannot be obtained from
metrics alone, but can be complementary to metrics
A Framework for Benchmarking of Feature Extraction Methods in Earth Observation image Analysis
No full textThe multitude of sensors used to acquire Earth Observation
(EO) images have led to the creation of an extremely
various collection of data. Along with appropriate methods
able to work with great amount of data, informat ion retrieval process requires algorithms to cope with a range of input imagery. Even if the geometry and the manner of creating Synthetic Aperture Radar (SAR) images are totally different than multispectral data, there are attempts of finding a common ground such that optical image indexing
algorithms can be applied for SAR data and vice versa.
Moreover, new concepts must be defined in order to obtain
satisfying results, enabling measurements and comparisons
between the extracted features [4]. Regarding this idea, the
goal is to develop an application capable to join feature
extraction algorithms and classification algorithms . Its
success will sustain the integration of a reliable EO data
search engine. This paper presents a framework for feature
extraction and classification aiming to support EO image
annotation. Weber Local Descriptors (WLD), Gabor filter
and Support Vector Machine (SVM) are combined in order
to define an application to be tested on both SAR and
optical data
A Taxonomy for High Resolution SAR Images
No full textThis paper proposes a semantic annotation conducted on a
large number of scenes containing high resolution synthetic
aperture radar (SAR) images. This investigation has an
important impact in applications such as classification of
urban areas, infrastructure, industrial sites, military sites,
landscape and agriculture. The proposed taxonomy can
serve as basis for building a semantic catalogue for Earth
Observation (EO) images. Finally, a set of queries based on
these semantics can be defined and are planned to be
integrated into the new system developed at DLR
Application of Visual Data Mining for Earth Observation Use Cases
No full textThis paper presents an application of visual data mining
technique to Earth-Observation images for exploring very
large image archives. We present a visual data mining
workstation solution and create some use cases in order to
demonstrate its functionality. This tool allows interactive
exploration and analysis of very large, high complexity,
and non-visual data sets stored into a database by using
human-machine communication. The tool relies on image
processing components that transform the image content to
primitive feature vectors and a graphical user interface, which allows the exploration of the entire image archive. The use cases are based on Synthetic Aperture Radar images, digital orthophotos and photos in-situ
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