The new ESA TomoSense campaign aims to explore the retrieval of biophysical quantities over forests for different acquisition geometries and radar parameters. Tomographic SAR acquisitions are currently being carried out using different wavelengths, both monostatic and bistatic systems and opposite views. This work presents the current advances in the analyses and calibration of the TomoSense data stacks to make them suited for scientific analyses. Airborne monostatic P-band acquisitions as received by MetaSensing presented artifacts connected to the acquisition geometry. Coherence and phase fluctuations were compensated thus obtaining clean tomographic reconstructions and a clear identification of the terrain level. Bistatic L-band data are expected to be available in short time as well.
Landslides are serious geohazards in Three Gorges area, China especially after the impoundment of Three Gorges Reservoir.It is very urgent to monitoring the landslides for early warning or disaster prevention purpose.In this paper, phase based methods such as traditional differential InSAR and small baseline subset method were used to investigate slow moving landslides.Point-like targets offset tracking (PTOT) was used to investigate fast moving landslides.Furthermore, in order to describe the displacement on landslide, two TerraSAR-X datasets obtained from different descending orbits were combined to obtain the three dimensional displacements on Shuping landslides with the PTOT measurements in the azimuth and range direction.
Registration of two or more images of the same scene is an important procedure in INSAR image processing that seeks to extract differential phase information not obtainable from each one of these images. Meanwhile, the accuracy of this step is crucial to the reliability of subsequent image processing and final results of the data processing chain. Based on some conventional INSAR registration methods, this paper presents an approach integrating correlation-registration and least square-registration to attain sub-pixel precision. Furthermore, experimentations implemented on test site prove validity of the registration method. Finally, some significant conclusions are made by the experiment results.
Impervious surface is a significant factor in monitoring urban development and environmental quality. However, accurate and cost-effective extraction of impervious surface is still a challenge. In light of the increasing availability of multisource remote sensing data from different imaging sensors, this study developed a method to map large-area impervious surface percentage at the sub-pixel level using multi-source remote sensing data. A case study in Shenzhen was conducted for this purpose based on a classification and regression tree (CART) algorithm to SPOT, Landsat ETM+ images. Experiment results indicate that both of the data are capable of mapping urban impervious surface percentage (ISP) with a reasonable accuracy, but the SPOT image has a better performance of impervious surface percent (ISP) estimation accuracy owing to its higher spatial resolution compared with Landsat ETM+.
Quantifying the kinematic evolution patterns of mountain glaciers near Yarlung Tsanpo River performs a major role in evaluating the glacial instability and the secondary disasters. For the Sedongpu Basin near the Yarlung Tsanpo River Valley, the dramatic geomorphic landscape variations triggered by the ice-rock avalanche events were visually identified as the dominant texture deficiencies in time-series optical images. To improve the image correlation quality broken by these image texture deficiencies, the Landsat-8/Sentinel-2 optical images were divided into different groups, then a stepwise combination strategy was innovatively proposed to derive the glacier time-series displacement velocities in different temporal stages. The standard deviations (STD) of the optical measurements in the stable area maintained around 0.04 m/yr for the normalized displacement velocity and maintained from 0.6 to 1.7 m for the cumulative displacement time series. The obvious variations in glacier displacement velocity were identified before each collapse event. Subsequently, the offset-tracking procedures were performed on 7 Sentinel-1A Synthetic Aperture Radar (SAR) images to acquire the range and azimuth displacement velocities. To better reveal the dynamic mechanism of the glacier activity, the three-dimensional (3D) glacial displacement velocity was also derived by using optical and SAR results. The precipitation, temperature, and seismic activities were assumed as the main triggering factors of controlling the glacial dynamic mechanism and final collapse events. Additionally, the dynamic mechanism of the active glaciers in Sedongpu Basin conformed to a power law, which was limited by the changes of the internal stress friction force on the sliding base surface. The aim of this study is to shed a light on interpreting the precursory displacement patterns and their implicit failure mechanism of these ice-rock avalanche events with the conventional freely optical and SAR observations.
In order to obtain the higher classification accuracy in specific categories for the different feature subset, a hierarchical classification algorithm based on Feature Selection is proposed, and is used for synthetic aperture radar (SAR) image classification, and feature selection is achieved by Genetic algorithm. The algorithm has two main characteristics: one is hierarchical classification which consists of many two-class classifier, and the two-class classifier is trained by the optimal feature subset which is selected according to different categories; the second is the classifier of support vector machine SVM (Support Vector Machine); the two is Genetic algorithm which can search out the optimal feature subset and parameters of support vector machine that is most suitable for the category, by unified coding the feature set and the parameters of SVM to constitute the Chromosome. The experiment on the first polarimetric SAR data show that the algorithm can obtain higher classification accuracy rate.
Recently, convolutional neural networks (CNNs) achieve impressive results on remote sensing scene classification, which is a fundamental problem for scene semantic understanding. However, convolution, the most essential operation in CNNs, restricts the development of CNN-based methods for scene classification. Convolution is not efficient enough for high-resolution remote sensing images and limited in extracting discriminative features due to its linearity. Thus, there has been growing interest in improving the convolutional layer. The hardware implementation of the JPEG2000 standard relies on the lifting scheme to perform wavelet transform (WT). Compared with the convolution-based two-channel filter bank method of WT, the lifting scheme is faster, taking up less storage and having the ability of nonlinear transformation. Therefore, the lifting scheme can be regarded as a better alternative implementation for convolution in vanilla CNNs. This paper introduces the lifting scheme into deep learning and addresses the problems that only fixed and finite wavelet bases can be replaced by the lifting scheme, and the parameters cannot be updated through backpropagation. This paper proves that any convolutional layer in vanilla CNNs can be substituted by an equivalent lifting scheme. A lifting scheme-based deep neural network (LSNet) is presented to promote network applications on computational-limited platforms and utilize the nonlinearity of the lifting scheme to enhance performance. LSNet is validated on the CIFAR-100 dataset and the overall accuracies increase by 2.48% and 1.38% in the 1D and 2D experiments respectively. Experimental results on the AID which is one of the newest remote sensing scene dataset demonstrate that 1D LSNet and 2D LSNet achieve 2.05% and 0.45% accuracy improvement compared with the vanilla CNNs respectively.
In this paper we investigate the classification performance of the compact polarimetric interferometric SAR (C-PolInSAR). The stressed compact modes are π/4 mode and CTLR mode, due to DCP mode equivalent to CTLR mode in theory. First, we provide a state-of-art of the C-PolInSAR modes, and present the different reconstruction algorithms aiming at recovering the full PolInSAR information from the observed C-PolInSAR dataset. Then, an assessment on the classification performance of the C-PolInSAR modes are carried out based on the Wishart ML classifier. Our emphasis is comparisons between the classification results of the C-PolInSAR modes and the corresponding reconstruction modes, versus the dual PolInSAR (D-PolInSAR) and the full PolInSAR (F-PolInSAR) systems. We test on the first PolInSAR dataset in China: CETC38 airborne data. Experimental results show that the C-PolInSAR has a promising application in the terrain classification, and it is an efficient alternative to the F-PolInSAR and the D-PolInSAR.
The orbit of TerraSAR-X is strictly controlled and well known. Based on this information, a very high absolute accuracy in geo-locating TerraSAR-X data is possible and has been demonstrated before. In this manuscript, we present an experimental validation of this, demonstrating the very high level of accuracy achievable using only the atmospheric phase delay information provided in the header files of TerraSAR-X.
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