Land-cover Classification in SAR Images using Dictionary Learning

Date

2015-09-24

Author

Aktas, Gizem
Bak, Cagdas
Nar, Fatih
Sen, Nigar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

250
views

0
downloads

Land-cover classification in Synthetic Aperture Radar (SAR) images has significance in both civil and military remote sensing applications. Accurate classification is a challenging problem due to variety of natural and man-made objects, seasonal changes at acquisition time, and diversity of image reconstruction algorithms.. In this study, Feature Preserving Despeckling (FPD), which is an edge preserving total variation based speckle reduction method, is applied as a preprocessing step. To handle the mentioned challenges, a novel feature extraction schema combined with a super-pixel segmentation and dictionary learning based classification is proposed. Computational complexity is another issue to handle in processing of high dimensional SAR images. Computational complexity of the proposed method is linearly proportional to the size of the image since it does not require a sliding window that accesses the pixels multiple times. Accuracy of the proposed method is validated on the dataset composed of TerraSAR-X high resolutions spot mode SAR images.

Subject Keywords

SAR, Land-cover classification, Dictionary learning, Sparse representation, Superpixel

URI

https://hdl.handle.net/11511/67440

DOI

https://doi.org/10.1117/12.2195773

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Ship detection in synthetic aperture radar (SAR) images by deep learning Ayhan, Oner; Sen, Nigar (2019-01-01) In this paper, we propose a Convolutional Neural Network (CNN) based method to detect ships in Synthetic Aperture Radar (SAR) images. The architecture of proposed CNN has customized parts to detect small targets. In order to train, validate and test the CNN, TerraSAR-X Spot mode images are used. In the phase of data preparation, a GIS (Geographic Information System) specialist labels ships manually in all images. Later, image patches that contain ships are cropped and ground truths are also obtained from pr...
Region Based Target Detection Approach for Synthetic Aperture Radar Images and Its Parallel Implementation Nar, Fatih; Demirkesen, Can; Okman, O. Erman; ÇETİN, MÜJDAT (2012-04-26) Automatic target detection (ATD) methods for synthetic aperture radar (SAR) imagery are sensitive to image resolution, target size, clutter complexity, and speckle noise level. However, a robust ATD method needs to be less sensitive to the above factors. In this study, a constant false alarm rate (CFAR) based method is proposed which can perform target detection independent of image resolution and target size even in heterogeneous background clutter. The proposed method is computationally efficient since cl...
A NOVEL FEATURE EXTRACTION METHOD FOR THE CLASSIFICATION OF SAR IMAGES Aytekin, Orsan; Koc, Mehmet; Ulusoy, İlkay (2012-07-27) This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining intensity information of pixels with spatial information and structural relationships. Unlike classical approaches which define a static neighborhood and relate spatial information for each center pixel to all the pixels within that window, the local primitives (LPs) proposed in this study provide us with an adaptive neighborhood for each pixel. LPs c...
Information Theoretic SAR Boundary Detection with User Interaction Demirkesen, Can; Leloğlu, Uğur Murat (2015-09-24) Detection of region boundaries is a very challenging task especially in the presence of noise or speckle as in synthetic aperture radar images. In this work, we propose a user interaction based boundary detection technique which makes use of B-splines and well-known powerful tools of information theory such as the Kullback-Leibler divergence (KLD) and Bhattacharyya distance. The proposed architecture consists of the following four main steps: (1) The user selects points inside and outside of a region. (2) P...
Vessel Classification on UAVs using Inertial Data and IR Imagery Demir, H. Seckin; Akagündüz, Erdem; Pakin, S. Kubilay (2015-01-01) In this study, a civilian ship dataset is constructed via images captured by an infrared camera on an unmanned flying vehicle. By using this dataset and synchronized inertial data (UAV altitude and orientation, gimbal angles), a vessel classification method is proposed. The method first calculates the ship base length in meters by using segmented ship image and inertial data. By fusing the descriptors obtained from the segmented ship images and estimated ship base length, vessel classification is performed.

Citation Formats

G. Aktas, C. Bak, F. Nar, and N. Sen, “Land-cover Classification in SAR Images using Dictionary Learning,” 2015, vol. 9642, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67440.