Temporal Synthetic Aperture Radar Images Research Articles

Winter wheat planting area extraction using SAR change detection

ABSTRACT With its all-day and all-weather imaging capability, Synthetic Aperture Radar (SAR) has shown great potential in crop identification. This letter puts forward a novel method of extracting winter wheat planting area by means of SAR change detection. Based on the phenological differences between winter wheat and other crops, the Difference Image (DI) is generated firstly by the different temporal SAR images. Then, Object Markov Random Field (OMRF) model is used to DI pre-classification. Finally, Convolutional Neural Network (CNN) is utilized to obtain the better area extraction result of winter wheat. The area extraction accuracy is proved by the field survey data, with an accuracy of 90.53%. This research can provide a new idea and method for agricultural remote sensing monitoring.

Imbalanced Learning-Based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net

Change detection is a quite challenging task due to the imbalance between unchanged and changed class. In addition, the traditional difference map generated by log-ratio is subject to the speckle, which will reduce the accuracy. In this letter, an imbalanced learning-based change detection is proposed based on PCA network (PCA-Net), where a supervised PCA-Net is designed to obtain the robust features directly from given multitemporal synthetic aperture radar (SAR) images instead of a difference map. Furthermore, to tackle with the imbalance between changed and unchanged classes, we propose a morphologically supervised learning method, where the knowledge in the pixels near the boundary between two classes is exploited to guide network training. Finally, our proposed PCA-Net can be trained by the data sets with available reference maps and applied to a new data set, which is quite practical in change detection projects. Our proposed method is verified on five sets of multiple temporal SAR images. It is demonstrated from the experiment results that with the knowledge in training samples from the boundary, the learned features benefit change detection and make the proposed method outperform than supervised methods trained by randomly drawing samples.

Object-based multiscale method for SAR image change detection

This paper proposed an object-based multiscale method for synthetic aperture radar (SAR) image change detection based on the statistical model. Rather than the pixel-based analysis conducted in the traditional way, the object-based image analysis was employed to take a collection of pixels as the unit of analysis, which reduced small spurious changes and was less strict relative to registration. In addition, a multiscale concept was adopted to exhibit the inherent multiscale characteristics of the target. To achieve object-based, multiscale change detection results, the multidate segmentation was performed on two temporal SAR images and extended to a set of suitable scales. Then, the Edgeworth series expansion was employed to estimate the probability density function, and the Kullback–Leibler divergence was adopted to calculate the distance between pairs of pixel collections. Next, the divergence index maps were divided into changed and unchanged classes to obtain change detection results for each scale. Finally, the subresults were combined to obtain a more accurate detection result. The experimental results obtained using real data demonstrated the effectiveness of the proposed method.

Multicontextual Mutual Information Data for SAR Image Change Detection

How to produce the difference data of the two temporal images is a crucial factor in image change detection. In this letter, we propose multicontextual mutual information data (MMID) based on the bivariate Gaussian distribution (BGD) for synthetic aperture radar (SAR) image change detection and illustrate their superiorities over the classical difference data. MMID, which are an improved form of image spatial mutual information, are constructed based on the quadrilateral Markov random field (QMRF) and can be factored into the linear combination of the entropies. Then to adapt MMID to the change detection, we construct the 2-D entropies based on the BGD. In this way, MMID are able to capture the intertemporal statistical dependence of the two temporal images and thus can be taken as the feature-level difference data rather than the pixel-level data. The maximum-likelihood method, the automatic threshold method, and the Markov random field method are performed on the MMID of the real two temporal SAR images for the change detection. Experimental results demonstrate the superiorities of MMID over the traditional difference data.

A New Coherent Similarity Measure for Temporal Multichannel Scene Characterization

This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolInSAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection.

Fusion of features in multi-temporal SAR imagery to detect changes in urban areas

The Dempster-Shafer (D-S) algorithm is improved to fuse different features of multi-temporal Synthetic Aperture Radar (SAR) images to detect changes in urban areas. Firstly, D-S theory is developed by not only considering the certainty of the sources, but also considering the average support of the sources to different subsets in the frame of discernment, in the process of evidence combination. In this way, it can assign conflict information in deferent sources reasonably and present a more reliable combination result. Secondly, amplitude ratio and distance between probability density distribution functions in Pearson graph of different temporal SAR images are extracted to present the change features in different scales. Lastly, the improved D-S algorithm is applied to fuse the two different features to detect change information of SAR images. An example of the Shanghai Lujiazui area using European Remote Sensing 2 (ERS-2) SAR images demonstrates the accuracy of the improved fusion algorithm.

Mapping and monitoring land cover types in Corumbiara area, Brazilian Amazônia, using Landsat TM and JERS-1 SAR

This paper discusses the use of a Landsat Thematic Mapper (TM) and JERS-1 Synthetic Aperture Radar (SAR) time-series for mapping and monitoring land cover types in a test site in the region of Corumbiara, Rondonia State, western Brazilian Amazonia. In order to support JERS-1 data analysis, land cover maps were obtained by digital classification of Landsat TM images acquired from 1993 to 1997 period, following a procedure based on image segmentation, unsupervised classification, and post-classification image edition. The comparison of these products with JERS-1 temporal images shows that clear-cut areas are well-identified presenting a low backscattering response as expected. On the other hand, areas that have been cleared and even burned but with remaining forest material left on the ground present high backscattering response. Considering these observations and user interpretation expertise, temporal SAR images present an effective source of information for mapping and monitoring deforestation, especially in areas covered by clouds with restricted use of optical data.

Potential of Multitemporal ERS-2 SAR Imagery for Land Use Mapping in Coastal Zone of Shangyu City, China

Abstract Scarce land resources and growing population are compelling issues for intensively developing coastal zones in China. During the past 30 years, many coastal tidelands have been enclosed for cultivation and development. To plan and implement large-scale reclamation programs in coastal zones, timely and reliable information concerning the nature, extent and physicochemical characteristics of the coastal saline soils land is essential. Because extracting useful information from optical remote sensing imagery is often hampered by the presence of clouds and wet weather, in this study we used multitemporal European remote sensing satellite (ERS)–2 PRI synthetic aperture radar (SAR) images only for coastal land use mapping. The SAR multitemporal backscattering characteristics of main land use categories were first investigated after pseudocolor synthesizing of three temporal SAR images, extracting dikes and partitioning the study area into six subzones according to the reclamation years. Next, an iterat...

Potentials for change detection using seasat synthetic aperture radar data

The use of synthetic aperture radar (SAR) images for detecting change on the earth's surface is highly dependent on target orientation, azimuth angle, and sensor depression angle. SAR data can be used for change detection when consistency is maintained in radar wavelength, polarization, azimuth directions, and off-nadir depression angle. The interaction of these parameters and the imaged surface for change detection are shown in examples drawn from (1) Los Angeles, CA, (2) southern Florida, (3) Imperial Valley, CA, (4) a desert region west of Tucson, AZ, and (5) western Kansas. SAR imagery is used to emphasize the geometric form, and roughness, of the earth's surface. As changes in the roughness of the surface occur over time, temporal SAR images will indicate those differences. Several guidelines for change detection studies using imaging radar are derived from the examples.