Taoli Yang

Assessment of Snow Status Changes Using L-HH Temporal-Coherence Components at Mt. Dagu, China

Multitemporal Phased Array type L-band Synthetic Aperture Radar (PALSAR) horizontally transmitted and horizontally received (HH) coherence data was decomposed into temporal-coherence, spatial-coherence, and thermal noise components. The multitemporal data spanned between February and May of 2008, and consisted of two pairs of interferometric SAR (InSAR) images formed by consecutive repeat passes. With the analysis of ancillary data, a snow increase process and a snow decrease process were determined. Then, the multiple temporal-coherence components were used to study the variation of thawing and freezing statuses of snow because the components can mostly reflect the temporal change of the snow that occurred between two data acquisitions. Compared with snow mapping results derived from optical images, the outcomes from the snow increase process and the snow decrease process reached an overall accuracy of 71.3% and 79.5%, respectively. Being capable of delineating not only the areas with or without snow cover but also status changes among no-snow, wet snow, and dry snow, we have developed a critical means to assess the water resource in alpine areas.

Study on a Novel Multiple Elevation Beam Technique for HRWS SAR System

With the improvement of resolution and image swath, the received data amount of spaceborne synthetic aperture radar (SAR) system is increasingly large and imposes more stringent requirement for the satellite payload and transmission link. In this paper, a novel multiple elevation beam (MEB) SAR and the processing scheme are studied to reduce the data amount. The detailed system design and the main procedures are described, and the explicit mathematic expression of the scheme is derived. Furthermore, an exemplary SAR system is provided and the simulation results are obtained to confirm the effectiveness of our proposal. Also, the performance of the MEB SAR system is analyzed in depth, from the perspectives of data amount, signal to noise ratio (SNR), and range ambiguity to signal ratio (RASR).

Investigation of snow cover change using multi-temporal PALSAR InSAR data at Dagu Glacier, China

The aim was to study seasonal snow and permanent snow variation in alpine regions using coherence component data derived from a multi-temporal of PALSAR InSAR data. With coherence decomposition technique, we obtained the multi-temporal data of temporal-coherence component near Mt. Dagu, China, where vegetated surface, seasonal snow cover or grazing area, and permanent snow cover exist. The variation of temporal-coherence component through time indicated changes of snow cover and status within the grazing zone and permanent snow area or areas above tree line. After the analyses of the temporal-coherence components from January to February, February to April, April to May, and January to May of 2008, we were able to identify snow status and change of snow cover above local tree line. The overall accuracy level greater than 71% was achieved in the identification when compared to those derived from multi-temporal TM images of Landsat 5. The results were promising.

Sar target configuration recognition using class-dependent locality preserving projections

Locality preserving projections (LPP) can preserve the local structure of the datasets effectively. However, it is not capable of separating the samples that are close to each other in the high-dimensional space but belong to different classes. Focusing on the problem, a class-dependent locality preserving projections (CDLPP) algorithm is proposed in this paper. The class information is embedded into the LPP model, and the similarity matrix and the difference matrix are constructed according to the class information. The similarity matrix is utilized to preserve the local structure of the samples belong to the same class, whereas the difference matrix is utilized to separate the samples that are close to each other in the high-dimensional space but belong to different classes. Experiments are conducted using the moving and stationary target acquisition and recognition (MSTAR) database, the results verify the effectiveness of the proposed algorithm.

Classification based on deep convolutional neural networks with hyperspectral image

Hyperspectral image (HSI) is usually composed of hundreds of bands which contain very rich spatial and spectral information. However, the high-dimensional data may lead to the curse of dimensionality phenomenon when it is used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we developed a deep learning classification framework based on the spectral and spatial information of hyperspectral image. Firstly, the deep learning features in different layers could be extracted automatically. Secondly, based on the learned deep learning features, we could obtain the classification of hyperspectral image with logistic regression (LR) classifier. Finally, we compared our approach with other methods including quadratic discriminant analysis with the multilevel logistic spatial prior (QDAMLL), logistic discriminant analysis with the multilevel logistic spatial prior (logDAMLL), linear discriminant analysis with the multilevel logistic spatial prior (LDAMLL), subspace multiclass logistic regression with the multilevel logistic spatial prior (MLRsub MLL), support vector machine on extended morphological profiles (SVM/EMP), support vector machine on expectation maximization and post-regularization (SVM-EM-PR). The experimental results showed that our method obtained the optimum accuracy, which was better than the other six approaches. And the OA was up to 99.39%. Therefore, the deep convolutional neural networks (DCNNs) is a robust method for land use classification with hyperspectral image. Index Terms — Classification; deep convolutional neural networks; hyperspectral image.

High resolution rotating fan-beam scatterometer imaging based on sparse recovery

High resolution synthetic aperture imaging using rotating fan-beam scatterometers is studied. First, the working mode is presented. Then, the relationships among pulse repetition frequency, the number of coherent pulses and the unambiguous swath are discussed. Considering the sparsity of the imaging region and the limited number of pulses, we adopt the sparse recovery method to obtain the target images. By utilizing multiple frequency systems, the unambiguous imaging swath is enlarged. Finally, the simulated results confirm the proposed method.

A novel algorithm to estimate moving target velocity for a spaceborne HRWS SAR/GMTI system

A novel algorithm to estimate moving target velocity for a spaceborne high resolution and wide swath (HRWS) synthetic aperture radar (SAR)/ground moving target indication (GMTI) system is presented. To retain the power of the moving target, one needs to know the velocity of the moving target before clutter suppression and spectrum reconstruction. According to the relationship between the velocity and cone angles, the estimation of velocity is transformed into the estimation of direction-of-arrival (DOA) of the received signal using the sparse DOA technique. If the clutter is ignorable, the algorithm is directly applied to the received signal. Otherwise a preprocessing based block matrix is performed. Simulated results confirm the effectiveness of the proposed algorithm.

A baby step for China but a giant leap for humans: Three basic issues with Chinese initiative of moon-based earth observation SAR system

A Moon-based synthetic aperture radar (SAR) system can provide large-scale, long-term, and constant earth observation (EO). Nevertheless, several problems should be solved before implementation. The problems include the ultra-small range of antenna viewing angles, the largest cell size of SAR allowed without the consideration of range cell migration and the related antenna size, and the decorrelation caused by long integration time. Although the moon-based EO system is a concept at present, with a baby but concrete and persistent step, the giant leap for human beings will be achieved.