422 research outputs found
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
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SAR object classification using the DAE with a modified triplet restriction
Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification
This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR\u27s airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/170
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
Classification of polarimetric synthetic aperture radar (PolSAR) images is an
active research area with a major role in environmental applications. The
traditional Machine Learning (ML) methods proposed in this domain generally
focus on utilizing highly discriminative features to improve the classification
performance, but this task is complicated by the well-known "curse of
dimensionality" phenomena. Other approaches based on deep Convolutional Neural
Networks (CNNs) have certain limitations and drawbacks, such as high
computational complexity, an unfeasibly large training set with ground-truth
labels, and special hardware requirements. In this work, to address the
limitations of traditional ML and deep CNN based methods, a novel and
systematic classification framework is proposed for the classification of
PolSAR images, based on a compact and adaptive implementation of CNNs using a
sliding-window classification approach. The proposed approach has three
advantages. First, there is no requirement for an extensive feature extraction
process. Second, it is computationally efficient due to utilized compact
configurations. In particular, the proposed compact and adaptive CNN model is
designed to achieve the maximum classification accuracy with minimum training
and computational complexity. This is of considerable importance considering
the high costs involved in labelling in PolSAR classification. Finally, the
proposed approach can perform classification using smaller window sizes than
deep CNNs. Experimental evaluations have been performed over the most
commonly-used four benchmark PolSAR images: AIRSAR L-Band and RADARSAT-2 C-Band
data of San Francisco Bay and Flevoland areas. Accordingly, the best obtained
overall accuracies range between 92.33 - 99.39% for these benchmark study
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Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Electromagnetic Wave Theory and Applications
Contains table of contents for Section 3 and reports on five research projects.U.S. Department of Transportation Contract DTRS-57-88-C-00078TTD13U.S. Department of Transportation Contract DTRS-57-88-C-00078TTD30Defense Advanced Research Projects Agency Contract MDA972-90-C-0021Digital Equipment CorporationIBM CorporationJoint Services Electronics Program Contract DAAL03-89-C-0001Joint Services Electronics Program Contract DAAL03-92-C-0001Schlumberger-Doll ResearchU.S. Navy - Office of Naval Research Grant N00014-90-J-1002U.S. Navy - Office of Naval Research Grant N00014-89-J-1019National Aeronautics and Space Administration Grant NAGW-1617National Aeronautics and Space Administration Grant 958461National Aeronautics and Space Administration Grant NAGW-1272U.S. Army Corp of Engineers Contract DACA39-87-K-0022U.S. Navy - Office of Naval Research Grant N00014-89-J-110
Terrain classification in SAR images using principal components analysis and neural networks
Includes bibliographical references.The development of a neural network-based classifier for classifying three distinct scenes (urban, park and water) from several polarized SAR images of San Francisco Bay area is discussed. The principal component (PC) scheme or Karhunen-Loeve (KL) transform is used to extract the salient features of the input data, and to reduce the dimensionality of the feature space prior to the application to the neural networks. Employing PC scheme along with polarized images used in this study, led to substantial improvements in the classification rates when compared with previous studies. When a combined polarization architecture is used the classification rate for water, urban and park areas improved to 100%, 98.7%, and 96.1%, respectively
Vehicle Classification Using Neural Network in Forward Scattering Radar
This thesis unveils the potential and utilization of Neural Network (NN) in radar
applications for target classification. The radar system under test is a special of its kind
and known as Forward Scattering Radar (FSR). FSR is a special type of bistatic radar
which the transmitted energy is scattered by a target and the target is so close to the
transmitter-receiver baseline. Recent works had shown that FSR can be effectively used
for classification, but the result can be further improved by using advance classification
method. To proof this, result from FSR experiment were used. The target used for this
experiment is a ground vehicle which is represented by typical public road transport.
New features from raw radar signal were determined and extracted manually prior to
classification process using Neural Network (NN). Two types of features in the time and
frequency domain signature were examined, namely time required for counting zero
crossings, first main lobe width, second main lobe- width and the number of lobes.
Multilayer perceptron (MLP) back-propagation neural network trained with back propagation algorithm was implemented and analyzed. In NN classifier, the unknown
target is sent to the network trained by the known targets to attain the accurate output.
Two tasks of classifications are analyzed. The first task is to recognize the exact type of
vehicle, four vehicle types were selected: Vauxhall Astra, Renault Traffic, Vauxhall
Combo and Honda Civic. The second task is to group vehicle into their categories:
small, medium and large. The proposed NN provides high percentage of successful
classification which is 90% and 98% of overall data was correctly classified in vehicle
recognition and vehicle categorisation respectively. The result presented show that NN
can be effectively employed in FSR system as a classification method
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