Fang Shang

Quaternion Neural-Network-Based PolSAR Land Classification in Poincare-Sphere-Parameter Space

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.

Averaged Stokes Vector Based Polarimetric SAR Data Interpretation

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.

Use of Poincare sphere parameters for fast supervised PolSAR land classification

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.

Adaptive land classification and new class generation by unsupervised double-stage learning in Poincare sphere space for polarimetric synthetic aperture radars

Polarimetric satellite-borne synthetic aperture radar (PolSAR) is expected to provide land usage information globally and precisely. In this paper, we propose a unsupervised double-stage learning land state classification system using a self-organizing map (SOM) that utilizes ensemble variation vectors. We find that the Poincare sphere parameters representing the polarization state of scattered wave have specific features of the land state, in particular, in their ensemble variation rather than spatial variation. Experiments demonstrate that the proposed PolSAR double-stage SOM system generate new classes appropriately, resulting in successful fine land classification and/or appropriate new class generation.

Three-Dimensional Imaging Method Incorporating Range Points Migration and Doppler Velocity Estimation for UWB Millimeter-Wave Radar

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.

Proposal of adaptive land classification using quaternion neural network with isotropic activation function

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.

Considerations on C/T matrix-based polsar land classification and explorations on stokes vector-based method

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.

Effect of coordinate rotation on stokes vector based polarimetric SAR data interpretation

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.

Unsupervised Land Classification by Self-organizing Map Utilizing the Ensemble Variance Information in Satellite-Borne Polarimetric Synthetic Aperture Radar

Polarimetric satellite-borne synthetic aperture radar is expected to provide land usage information globally and precisely. In this paper, we propose a two-stage unsupervised-learning land state classification system using a self-organizing map (SOM) based on the ensemble variance. We find that the Poincare sphere parameters representing the polarization state of scattered wave have specific features of the land state, in particular, in their dispersion (or ensemble variance). We present two-stage clustering procedure to utilize the dispersion features of the clusters as well as the mean values. Experiments demonstrate its high capability of self-organizing and discovering classification based on the polarimetric scattering features representing the land states.

Combination use of multiple window sizes for stokes vector based polsar data interpretation

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.