In this work, we analyze the collapsed buildings in Mashikimachi, Japan, after the Kumamoto Earthquake. First, we show the results of distinguishing built-up area from natural target area and qualitatively observing the damage. Second, we show a quantitative analysis on the damage level by employing the recently proposed seven scattering model decomposition. Finally, we discuss the possibility for combining the built-up area detection and damage level analysis by using quaternion neural networks.

This work proposes a built-up area detecting method by analyzing the fluctuation of degree of polarization in frequency domain. The averaged detrended amplitude of the two dimensional fast Fourier transform is proposed for describing the fluctuating information, and serving as the new discriminator. The comparison results for ALOS2-PALSAR2 data of Hakodate area, San Francisco area, and Ebetsu area prove that the proposed method has stably higher detecting accuracy, basically higher than 90%, for various types of built-up areas, much fewer error responses, basically lower than 5%, in natural area, and better performance on recognizing small buildings from vegetation background. Two application examples, i.e., urbanization process observation of Weihe area, and collapsed buildings detection of Mashikimachi area after the Kumamoto earthquake further confirm its good performance.

This work proposes a new urban area interpretation method based on frequency-domain analysis of the degree of polar-ization information. The results generated by using ALOS2-PALSAR2 data sets indicate the high performance.

In previous works we have proposed many averaged Stokes vector (ASV) based features and used the ASV features for distinguishing coniferous and broad-leaved forests in success. Continuously, we are trying to apply proper ASV features for man-made target analysis. In this paper, we show the results on man-made target detecting and low-rise and high-rise buildings distinguishing. The experiments using ALOS2-PALSAR2 data of Hakodate area and Tokyo area prove the high performance of the proposed methods.

This article proposes a coniferous and broad-leaved forest distinguishing method using L-band polarimetric SAR data based on the structure-orientation parameter. The structure-orientation parameter is one of the averaged Stokes vector-based discriminators which is sensitive to the composition of equivalent horizontal and vertical structures. In the proposed method, the structure-orientation parameters is compensated by employing the scattered power information to remove the influence of the topography. The final distinguishing result is generated based on the statistical feature of the compensated parameters. The experiments using several sets of ALOS2-PALSAR2 level 1.1 data prove that the proposed method has high performance for forest-type distinguishing.

In this paper, first, the rationality of shifted angle value based orientation estimating methods is discussed. Such methods are theoretically hard to achieve high accuracy orientation angles. And, in the case of the actual orientation angle is big, the fluctuation of the estimation results in a local window will be too high to make a final decision. Second, a rough classification method for building area based on the fluctuation level of the shifted angle is suggested.

This letter proposes a data arrangement for fully polarimetric synthetic aperture radar (PolSAR). It is an essential novel method in the use of the rotation transformation in data interpretation. The key point of the proposal is employing a single pixel-based and selective rotation transformation for each pixel before the speckle filtering. The experimental results with ALOS2-PALSAR2 data show that the proposed data arrangement has much higher performance in recognizing double-bounce scattering in the man-made target area. At the same time, it is effective in avoiding the overestimation of double-bounce and/or surface scattering in natural target areas.

In this work, we will show that one of the Stokes-vector-based discriminator is highly useful for forest distinguishing. Using ALOS2-PALSAR2 data, we will show that the parameter has good performance for distinguishing coniferous forest and broad-leaved forest.

In this work, first the problems of current rotation transformations in PolSAR data interpretation will be discussed. Considering the problems, we propose a novel process for implementing the rotation transformation. The transformation will be implemented with single look for selected pixes. The experimental results show the good performance of the proposed method.

This paper proposes a novel data filtering algorithm for fully polarimetric synthetic aperture radar (PolSAR) based on the degree of polarization (DoP) information. First, we define the homogeneity degree and polarization independence degree using the DoP information, and propose a feature plane to characterize the target feature. Second, employing the feature plane, we categorize the targets into three types and assign specific filtering policy for each type to estimate the optimal filtering window sizes. Finally, the $T$ -matrices of fully PolSAR data are filtered using the windows with estimated optimal sizes. Compared with boxcar filter, refined Lee filter, scattering model-based filter, and improved sigma filter in processing ALOS2-PALSAR2 data, the proposed DoP-based algorithm presents the best filtering performance.

In our previous works, we have found the optimal window size for ALOS2-PALSAR2 data is 7 ×7 (or other window size settings including around 50 pixels). Such an optimal window size is fixed for the whole observation area. However, the optimal window size for different types of targets are actually different. In order to improve general interpretation performance, we propose a “target-wise” window size optimization algorithm. The process is like automatically zooming and finding the particular optimal window sizes for different target areas. The results show that the proposed algorithm has higher interpretation performance than fixed-type window size optimization.

Quaternion neural networks (QNNs) achieve high accuracy in polarimetric synthetic aperture radar classification for various observation data by working in Poincare-sphere-parameter space. The high performance arises from the good generalization characteristics realized by a QNN as 3-D rotation as well as amplification/attenuation, which is in good consistency with the isotropy in the polarization-state representation it deals with. However, there are still two anisotropic factors so far which lead to a classification capability degraded from its ideal performance. In this letter, we propose an isotropic variation vector and an isotropic activation function to improve the classification ability. Experiments demonstrate the enhancement of the QNN ability.

In this paper, we first determine the optimal window size for ALOS2-PALSAR2 data is 7 × 7. To preserve the accuracy of incoherent interpretation and the high resolution of original data, simultaneously, we proposed the combination use of various window sizes in Stokes vector based data interpretation. The experimental results show that the proposed method can provide interpretation results in success and can preserve much more target details than conventional fixed window size method.

High-resolution, short-range sensors that can be applied in optically challenging environments (e.g., in the presence of clouds, fog, and/or dark smog) are in high demand. Ultrawideband (UWB) millimeter-wave radars are one of the most promising devices for the above-mentioned applications. For target recognition using sensors, it is necessary to convert observational data into full 3-D images with both time efficiency and high accuracy. For such conversion algorithm, we have already proposed the range points migration (RPM) method. However, in the existence of multiple separated objects, this method suffers from inaccuracy and high computational cost due to dealing with many observed RPs. To address this issue, this letter introduces Doppler-based RPs clustering into the RPM method. The results from numerical simulations, assuming 140-GHz band millimeter radars, show that the addition of Doppler velocity into the RPM method results in more accurate 3-D images with reducing computational costs.

Previously, we have proposed a successful land classification method using a quaternion neural network (QNN) to process parameters based on Stokes vector representation. In this method, the activation function used in the QNN is anisotropic and is applied to input quaternions of which elements are separately and independently processed. In this paper, considering the isotropy of Poincare-sphere space, we propose a new isotropic activation function. Experimental results show that the QNN with the proposed activation function achieves more accurate land classification.

In this work, we discuss the effect of coordinate rotation on the proposed Stokes vector based PolSAR data interpretation algorithms. The core work for making clear the effects is finding the change regulations for the zero aperture/orientation routes and aperture/orientation triangles with coordinate rotations. We mathematically analyze and summarize the regulations. The analysis results have shown that such regulations are predicable. With these regulations, we can possibly further improve the Stokes vector based interpretation algorithms.

In modeling, prediction and control applications, the single-input-rule-modules (SIRMs) connected fuzzy inference method can efficiently tackle the rule explosion problem that conventional fuzzy systems always face. In this paper, to improve the learning performance of the SIRMs method, a neural structure is presented. Then, based on the least square method, a novel parameter learning algorithm is proposed for the optimization of the SIRMs connected neural-fuzzy system. Further, the proposed neural-fuzzy system is applied to the cooling and heating loads prediction which is a popular multi-variable problem in the research domain of intelligent buildings. Simulation and comparison results are also given to demonstrate the effectiveness and superiority of the proposed method.

In this paper, we propose a new polarimetric synthetic aperture radar (SAR) data interpretation method based on a locally averaged Stokes vector. We first propose a method to extract discriminators from all three components of the averaged Stokes vector. Based on the extracted discriminators, we build four physical interpretation layers with ascending priorities, i.e., the basic structure layer, the low-coherence targets layer, the man-made targets layer, and the low-backscattering targets layer. An intuitive final image can be generated by simply stacking the four layers in the priority order. We test the performance of the proposed method over Advanced Land Observing Satellite Phased Array type L-band SAR (ALOS-PALSAR) data. Experimental results show that the proposed method has high interpretation performance, particularly for skew-aligned or randomly distributed buildings and isolated man-made targets such as bridges.

In this paper, we elaborate several considerations on the possible factors restricting the accuracy of covariance/coherency (C/T) matrix-based unsupervised classification. Then we make an exploration on constructing Stokes vector-based unsupervised classification. The experimental results for Fujisusono area show that Stokes vector-based method can distinguish building and farmland targets more correctly. It also shows potential to distinguish vegetations with different height or thickness.

We propose a quaternion neural-network-based land classification in Poincare-sphere-parameter space. By representing the Stokes vector on/in the Poincare sphere geometrically, we construct two analysis parameters, namely, the position vector and the variation vector, to describe the feature of a pixel in test area. Then, by employing a quaternion feedforward neural network, we generate successful classification results for detecting lake, grass, forest, and town areas. In comparison with the conventional C-matrix-based methods, the proposed method has higher classification performance, especially in detecting forest and town areas. Moreover, the classification result of the proposed method is not influenced by height information. This fact suggests that the proposed classification method can be used for complicated terrains.

We present a joint estimator of the time of arrival (TOA) and angle of arrival (AOA) for impulse radio ultrawideband (UWB) systems in which an antenna array is employed at the receiver. The proposed method consists of two steps: 1) preliminary estimation of the TOA and the average power delay profile (APDP) using energy-based threshold crossing and log-domain least-squares fitting, respectively; and 2) joint TOA refinement and AOA estimation by local 2-D maximization of a log-likelihood function (LLF) that employs the preliminary estimates from the first step. The derivation of the LLF relies on an original formulation in which the superposition of images from secondary paths is modeled as a Gaussian random process, whose second-order statistical properties are characterized by a wideband space-time correlation function. In addition to the APDP, this function incorporates a special gating mechanism to represent the onset of the secondary paths, thereby leading to a novel form of the LLF. Closed-form expressions for the Cramer-Rao bound on the variance of the TOA and AOA estimators are also derived, which formally take into account pulse overlap through this gating mechanism. In simulation experiments based on multipath UWB channel models featuring both diffuse and directional image fields, our approach exhibits superior performance to that of a competing scheme from the recent literature.

A novel joint TOA and AOA estimator is proposed for impulse radio Ultra-Wideband (IR-UWB) systems, in which a uniform linear array of antennas is employed at the receiver. The proposed method consists of two steps: (1) coarse estimation of the TOA and the average power delay profile; (2) joint TOA refinement and AOA estimation by maximization of a novel log likelihood function (LLF) using the coarse estimates from the first step. The derivation of the LLF is based on an original approach in which the pulse image from the primary path is modeled as a deterministic component while the superposition of the images from the secondary paths is modeled as a Gaussian random process. In addition, a special gating mechanism is used to characterize the secondary paths, thereby leading to a previously unknown form of the LLF in step (2). According to simulation experiments based on standard UWB channel models, our approach exhibits superior performance to that of a competing scheme from the recent literature.

We propose the use of Poincare sphere parameters for a fast supervised PolSAR land classification. The scattering matrix is represented by a point which indicates the polarization states on/in Poincare sphere. Then, by analyzing the distribution features of the points, the test area is classified into, for example, four types of targets: lake, grass, town and forest. This analyzing process can be implemented by employing a neural network. The experimental result shows that the Poincare sphere parameters are highly useful for classification. It is possible that the method will contribute to reduce the computational complexity of PolSAR classification process and provide higher accuracy.

Sub-Nyquist maximum likelihood (ML)-based time of arrival (TOA) estimation methods for ultra-wideband (UWB) signals normally assume a priori knowledge of the UWB channel in the form of the average power delay profile (APDP). In practice however, and despite its importance, the APDP is not always available. To address this issue, we develop in this paper a joint estimator of TOA and APDP. Knowing that the APDP of a UWB channel usually consists of several clusters, each with specific exponential decay rate, a parametric APDP model of this type is employed. The parameters of this model are estimated via a least-squares fitting approach; then the estimated APDP is used to form a likelihood function and obtain a ML estimator of the TOA. Simulations show that the TOA estimated jointly in this way achieves a good accuracy in practical scenarios. The proposed APDP estimate can also help to boost the performance of previously reported TOA estimators that assume a priori APDP knowledge, although the proposed ML scheme generally offers superior performance.

This paper is devoted to the problem of global stabilization by output-feedback for a class of nonlinear systems with uncertain control coefficients, stable zero-dynamics and linearly unmeasured states dependent growth. By first introducing two kinds of appropriate state transformations, the original system is converted into the new system with deterministic virtual control coefficients and the separated zero-dynamics. Then, a suitable observer based on high-gain K-filters is constructed for the new system, and the backstepping design approach is successfully proposed to the output-feedback controller. It is shown that the global asymptotic stability of the closed-loop system can be guaranteed by the appropriate choice of the design parameters.