5,248 research outputs found
PATCH-BASED SAR IMAGE CLASSIFICATION: THE POTENTIAL OF MODELING THE STATISTICAL DISTRIBUTION OF PATCHES WITH GAUSSIAN MIXTURES
International audienceDue to their coherent nature, SAR (Synthetic Aperture Radar) images are very different from optical satellite images and more difficult to interpret, especially because of speckle noise. Given the increasing amount of available SAR data, efficient image processing techniques are needed to ease the analysis. Classifying this type of images, i.e., selecting an adequate label for each pixel, is a challenging task. This paper describes a supervised classification method based on local features derived from a Gaussian mixture model (GMM) of the distribution of patches. First classification results are encouraging and suggest an interesting potential of the GMM model for SAR imaging
A deep-neural-network-based hybrid method for semi-supervised classification of polarimetric SAR data
This paper proposes a deep-neural-network-based semi-supervised method for polarimetric synthetic aperture radar (PolSAR) data classification. The proposed method focuses on achieving a well-trained deep neural network (DNN) when the amount of the labeled samples is limited. In the proposed method, the probability vectors, where each entry indicates the probability of a sample associated with a category, are first evaluated for the unlabeled samples, leading to an augmented training set. With this augmented training set, the parameters in the DNN are learned by solving the optimization problem, where the log-likelihood cost function and the class probability vectors are used. To alleviate the “salt-and-pepper” appearance in the classification results of PolSAR images, the spatial interdependencies are incorporated by introducing a Markov random field (MRF) prior in the prediction step. The experimental results on two realistic PolSAR images demonstrate that the proposed method effectively incorporates the spatial interdependencies and achieves the good classification accuracy with a limited number of labeled samples
Remote sensing of earth terrain
In remote sensing, the encountered geophysical media such as agricultural canopy, forest, snow, or ice are inhomogeneous and contain scatters in a random manner. Furthermore, weather conditions such as fog, mist, or snow cover can intervene the electromagnetic observation of the remotely sensed media. In the modelling of such media accounting for the weather effects, a multi-layer random medium model has been developed. The scattering effects of the random media are described by three-dimensional correlation functions with variances and correlation lengths corresponding to the fluctuation strengths and the physical geometry of the inhomogeneities, respectively. With proper consideration of the dyadic Green's function and its singularities, the strong fluctuation theory is used to calculate the effective permittivities which account for the modification of the wave speed and attenuation in the presence of the scatters. The distorted Born approximation is then applied to obtain the correlations of the scattered fields. From the correlation of the scattered field, calculated is the complete set of scattering coefficients for polarimetric radar observation or brightness temperature in passive radiometer applications. In the remote sensing of terrestrial ecosystems, the development of microwave remote sensing technology and the potential of SAR to measure vegetation structure and biomass have increased effort to conduct experimental and theoretical researches on the interactions between microwave and vegetation canopies. The overall objective is to develop inversion algorithms to retrieve biophysical parameters from radar data. In this perspective, theoretical models and experimental data are methodically interconnected in the following manner: Due to the complexity of the interactions involved, all theoretical models have limited domains of validity; the proposed solution is to use theoretical models, which is validated by experiments, to establish the region in which the radar response is most sensitive to the parameters of interest; theoretically simulated data will be used to generate simple invertible models over the region. For applications to the remote sensing of sea ice, the developed theoretical models need to be tested with experimental measurements. With measured ground truth such as ice thickness, temperature, salinity, and structure, input parameters to the theoretical models can be obtained to calculate the polarimetric scattering coefficients for radars or brightness temperature for radiometers and then compare theoretical results with experimental data. Validated models will play an important role in the interpretation and classification of ice in monitoring global ice cover from space borne remote sensors in the future. We present an inversion algorithm based on a recently developed inversion method referred to as the Renormalized Source-Type Integral Equation approach. The objective of this method is to overcome some of the limitations and difficulties of the iterative Born technique. It recasts the inversion, which is nonlinear in nature, in terms of the solution of a set of linear equations; however, the final inversion equation is still nonlinear. The derived inversion equation is an exact equation which sums up the iterative Neuman (or Born) series in a closed form and, thus, is a valid representation even in the case when the Born series diverges; hence, the name Renormalized Source-Type Integral Equation Approach
A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar
A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877
Remote sensing of earth terrain
Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others
A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar
A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877
Selecting Optimal RBF Kernel with Machine Learning for Feature Extraction and Classification in SAR Images
Kernel methods are gaining popularity in image processing applications. The accuracy of feature extraction and classification on image data for a given application is greatly influenced by the choice of kernel function and its associated parameters. As on today there existing no formal methods for selecting the kernel parameters. The objective of the paper is to apply machine learning techniques to arrive at suitable kernel parameters and improvise the accuracy of kernel based object classification problem. The graph cut method with Radial Basis function (RBF) is employed for image segmentation, by energy minimization technique. The region parameters are extracted and applied to machine learning algorithm along with RBF2019;s parameters. The region is classified to be man made or natural by the algorithm. Upon each iteration using supervised learning method the kernel parameters are adjusted to improve accuracy of classification. Simulation results based on Matlab are verified for Manmade classification for different sets of Synthetic Aperture RADAR (SAR) Images
Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube
The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australias WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS.
Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection
1. INTRODUCTION
Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise.
Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australias Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves.
Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this bands value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2]
In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification.
2. PLATFORM
WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scenes resolution. Currently WASARD supports water detection on scenes from ESAs Sentinel-1 and JAXAs ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites.
3. METHODOLOGY
Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics:
Precision: a measure of what percentage of the points WASARD labels as water are truly water
Recall: a measure of what percentage of the total water cover WASARD was able to identify.
F1 Score: a harmonic average of the precision and recall scores
Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithms classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in
this paper were produced using the function wasard_plot(),
which is included in WASARD.
3.1 Algorithm Selection
The machine learning model used by WASARD is the
Linear Support Vector Machine (SVM). This model uses a
supervised learning algorithm to develop a classifier,
meaning it creates a vector which can be multiplied by the
vector formed by the relevant data bands to determine
whether a pixel in a SAR scene contains water. This
classifier is trained by comparing data points from selected
bands in a SAR scene to their respective labels, which in this
case are water or not water as given by the WOFS
algorithm. The SVM was selected over the Random Forest
model, which outperformed the SVM in training speed, but
had a greater classification time and lower accuracy, and the
Multilayer Perceptron Artificial Neural Network, which had
a slightly higher average accuracy than the SVM, but much
greater training and classification times.
Figure 1: Visual representation of the SVM Classifier.
Each white point represents a pixel in a SAR scene.
In Figure 1, the diagonal line separating pixels
determined to be water from those determined not to be
water represents the actual classification vector produced by
the SVM. It is worth noting that once the model has been
trained, classification of pixels is done in a similar manner
as in the thresholding method. This is especially true if only
one band was used to train the model.
3.1 Feature Selection
Sentinel-1 collects data from two bands: the
Vertical/Vertical polarization (VV) and the
Vertical/Horizontal polarization (VH). When 100 SVM
classifiers were created for each polarization individually,
and for the combination of the two, the following results
were achieved:
Figure 2: Accuracy of classifiers trained using different
polarization bands. Precision and Recall were measured
with respect to the values produced by WOFS.
Figure 2 demonstrates that using both the VV and VH
bands trades slightly lower recall for significantly greater
precision when compared with the VH band alone, and that
using the VV band alone is inferior in both metrics.
WASARD therefore defaults to using both the VV and VH
bands, and includes the option to use solely the VH band.
The VV polarizations lower precision compared to the VH
polarization is in contrast to results from previous research
and may merit further analysis [4].
3.2 Training a Classifier
The steps in training a classifier with WASARD are
1. Selecting two scenes (one SAR, one optical) with
the same spatial extents, and acquired close to
each other in time, with a preference that the
scenes are taken on the same day.
2. Using the WOFS algorithm to produce an array of
the detected water in the scene of optical data, to
be used as the labels during supervised learning
3. Data points from the selected bands from the SAR
acquisition are bundled together into an array with
the corresponding labels gathered from WOFS. A
random sample with an equal number of points
labeled Water and Not Water is selected to be
partitioned into a training and a testing dataset
4. Using Scikit-Learns LinearSVC object, the
training dataset is used to produce a classifier,
which is then tested against the testing dataset to
determine its precision and recall
The result is a wasard_classifier object, which has the
following attributes:
1. f1, recall, and precision: 3 metrics used to
determine the classifiers accuracy
2. Coefficient: Vector which the SVM uses to make
its predictions. The classifier detects water when
the dot product of the coefficient and the vector
formed by the SAR bands is positive
3. Save(): allows a user to save a classifier to the disk
in order to use it without retraining
4. wasard_classify(): Classifies an entire xarray of
SAR data using the SVM classifier
All of the above steps are performed automatically
when the user creates a wasard_classifier object.
3.3 Classifying a Dataset
Once the classifier has been created, it can be used to detect
water in an xarray of SAR data using wasard_classify(). By
taking the dot product of the classifiers coefficients and the
vector formed by the selected bands of SAR data, an array
of predictions is constructed. A classifier can effectively be
used on the same spatial extents as the ones where it was
trained, or on any area with a similar landscape. Whil
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