The model-based scattering power decomposition for POLSAR data is one of the attractive techniques because it makes understanding physical scattering mechanism easier. Many modified decomposition techniques are proposed. However, most of them still have the problem of mis-in urban area. In this report, we propose a modified technique using an oriented dihedral component to mitigate the problem. The novelty of this method is that it solves the problem of orientation angle estimation error in conventional methods. In this report, we clarify why conventional mis-decompositions occur with numerical examples and propose a new method to reduce the errors.

The space-based polarimetric synthetic aperture radar (PolSAR) image has been expected as a quantitative long-term monitoring tool owing to its distinctive reproducibility and sensitivity. This study devised an inversion technique of physical surface parameters from PolSAR data by considering an axis of comparison so as to realize a quantitative comparison in space and time. First, it was shown that the quantitative change monitoring cannot be achieved via popular PolSAR decomposition techniques owing to the unstable axes of comparison. Then, a novel inversion algorithm was devised by brute force with a solid axis of comparison in combination with an evaluation utilizing a feature of the Hermitian matrix. The technique was verified using numerical simulation for various surfaces such as vegetation, urban, and sea surfaces, demonstrating unique inversion with reasonable accuracy. The technique was also validated using real PolSAR images obtained by Advanced Land Observation Satellite (ALOS-2). Various objects located in different places were selected from a scene in San Francisco, whereas a deciduous forest was used in Hokkaido, Japan, to examine the time-variation of the parameters. The results were promising based on a scattering theory of PolSAR. Moreover, some distinguished statistical features were recognized in the forested area and should be studied in the future.

Owing to its high reproducibility, synthetic aperture radar (SAR) is a reliable change detection tool. Although several maximization techniques for maximizing the time variance of polarimetric SAR (PolSAR) data have been proposed, quantitative long-term change monitoring has never been achieved owing to the infeasibility of selecting a pair of polarization states for the transmitting and receiving antennas and their variation during monitoring. This study introduces polarimetric sensitivity, an index characterized by the time variation of received power. Polarimetric sensitivity can be maximized by selecting a physically realizable pair of polarization states. Then, the selected pair, i.e., an axis of comparison, is maintained constant throughout the observation period so that long-term continuous change can be quantitatively measured. The technique was verified first using a numerically simulated scattering power from a simple oriented dipole and then by analyzing real time-series PolSAR images obtained by the second Advanced Land Observation Satellite (ALOS-2). Observing the seasonal changes in a broad-leaf forest in Tomakomai Experimental Forest (TOEF) in Hokkaido, Japan, from winter to summer and the secular changes in the earthquake disaster area in Kumamoto, Japan, confirmed that the sensitivity of each pixel in the proposed scheme was maximized, and the received power reconstructed by the optimized polarization states exhibited continuous variation in time. Moreover, the optimization technique revealed that co-polarization was considered for most pixels in the forested and disaster regions to achieve optimal sensitivity.

To make polarimetric synthetic aperture radar (SAR) accessible to mainstream applications, the true composition ratio of scattering mechanisms within a radar backscatter must be accurately identified. To validate polarimetric SAR decomposition techniques, a novel multiparametric SAR observation combined with a theoretical model simulation has been applied to rice paddies having simple vegetation structure on smooth and flat surfaces, where the surface scattering was small. In this article, cabbage fields in Miura city in Japan were selected as outdoor-grown vegetables, so the effect of exposed ground surfaces must be taken into account. In combination with the multiparametric SAR observation, a dominant scattering mechanism is reliably isolated through the theoretical characterization of the data by a discrete scatterer model (DSM). Suppressing unnecessary effects by row structures of the crop field, the volume scattering from cabbage leaves is identified as a dominant scattering mechanism over most incidence angles. On the other hand, the surface scattering is dominant for copolarizations only at small incidence angles. The analysis also indicates the possibility that greater soil moisture could be kept by roots of mature cabbages. To demonstrate the validation of polarimetric decomposition by the results, a popular three-component decomposition was applied to the observation. Differences between the decomposition and the simulation results were analytically explained by features of the backscatter from cabbage fields. Model-based polarimetric decompositions shall be improved through this approach.

SAR remote sensing has been attractive as an efficient tool to monitor a vast expanse of the ocean due to its flexible operability with wide swath. In this context, many applications have been developed for ocean traffic monitoring based on conventional ship detection techniques. Nowadays, ScanSAR modes usually covers around 500 km of swath with coarse resolution more than 50 m which is hopeless to directly identify each detected ship. Because the swath width of ScanSAR modes is getting wider, it might be useful to know the distribution of ships as soon as possible. In this paper, an identification of a group of ships by ALOS-2 Scan-SAR data would be achieved by using various statistical indicators calculated from the images. The technique is applied to open water around Yamatotai in Japan Sea which is abundant for various fishes including squids and crabs. Each indicator is quantitatively justified.

This paper presents an efficient GPU-based algorithm to perform histogram analysis of sub-images for change detection in SAR images. It is designed to use three-stage parallelisms: the SAR images are sub-divided and distributed to all the GPU devices; raster-scans for the sub-divided images are parallelized with many thread-groups inside each GPU; and 32 threads inside the group cooperatively compute the 32 statistical elements one by one by reusing the sub-histograms generated from the overlapped pixels until the dynamic range is modified. The analyzer with quad-GPU takes 1.7 s, which is 5.2 and 57.5 times faster than the conventional analyzer and that with 32-threaded dual-CPU, respectively, when applied to the problem of flood-water detection in ALOS-2 images with 37305×26811 pixels.

TomoSAR is one of the methods which can perform three-dimensional (3-D) imaging with multiple SAR datasets by using Direction-of-Arrival (DOA) estimation techniques to reveal height distribution of scatterers. However, resolution of the TomoSAR is generally insufficient for height estimation of small objects on the ground surface. Also, if we employ a high resolution DOA estimation method for the height estimation, its performance is often degraded due to orbital error. To solve this problem, we employ ESPRIT algorithm and propose a new method named ESPRIT-TomoSAR. The ESPRIT algorithm is one of high resolution DOA estimation techniques using orthogonality between signal and noise subspace, it has not only high resolution performance equivalent to the MUSIC algorithm but also robustness for orbital error. In this paper, to verify effectiveness of the proposed method, we provide experimental results by using actual airborne SAR dataset. The experimental results show that the proposed method realizes high resolution and robustness for orbital error, and it is effective for a precise height estimation such as the structure analysis of buildings.

The determination of the accurate composition ratio of scattering mechanisms (volume scattering, double-bounce scattering, and surface scattering) within a radar backscatter is essential to validate current polarimetric decomposition techniques. Multiincidence angle and multipolarimetric synthetic aperture radar (MIMP SAR) observations at the X- and L-bands were applied to rice paddies at late vegetative stage in Niigata City in Japan in 2014 and 2016, respectively. In this paper, multifrequency MIMP SAR analysis is introduced based on the observation results. The approach, combined with theoretical characterization of the data by a discrete scatterer model, showed that rice panicles affect the backscatter from rice paddies. Contrary to expectation, an effect of transmissivity by using different bands is not obvious. The similar level of copolarization (HH and VV) backscatter at X- and L-bands could be explained by the effective size of rice panicles. They are the most characteristic scatters in rice paddy field with respect to multiple frequency polarimetric sensing. In addition, HV shows a distinct sensitivity to the mean orientation angle and the size of panicles regardless of the wavelength. The mean orientation angle affects the polarimetric randomness under azimuthal symmetry, whereas the size of panicles directly affects the attenuation of the volume scattering from the grains. The multifrequency MIMP SAR analysis also indicated the importance of considering the backscatter and attenuation in the interpretation of the backscattering cross section from vegetated fields.

SAR remote sensing has been expected as an efficient rice crop monitoring tool due to its flexible operability with wide swath. Though many researchers have intensively analysed the backscatter, there still exist non-negligible mysteries to extract physical parameters. To solve the problems, it is essential to conduct theoretical characterization of experimentally obtained radar backscatter from rice paddies. In this paper, the rice paddies were observed by airborne SAR's at X- and L-bands by systematically varying the observation parameters such as incidence angle and polarization. The obtained full polarimetric backscattering cross section in terms of incidence angles and wavelengths based on many sample points in Niigata city is discussed.

L-band airborne synthetic aperture radar observations were made over California shrublands to better understand the effects of soil and vegetation parameters on backscattering coefficient (σ0) for the period of 2011 to 2015. HH was always greater than VV, suggesting the importance of double-bounce scattering by the woody parts. However, the geometric and dielectric properties of the woody parts did not vary significantly over time. Instead the changes in vegetation water content (VWC) were observed to occur primarily in thin leaves that may not meaningfully influence absorption and scattering. Accordingly, unlike in the past literature, the VWC input of the plant to the model was formulated as a function of plant's dielectric property (water fraction) while the plant geometry remains static in time. A physically-based model for single scattering by discrete elements of plants successfully simulated the magnitude of the temporal variations in HH, VV, and HH/VV with a difference of less than 0.9 dB. The modeling results offer an explanation of why soil moisture correlated highly with σ0, which is that the dominant mechanisms for HH and VV are double-bounce scattering by trunk, and soil surface scattering, respectively.

The paper describes hardware performances of PALSAR-3 onboard ALOS-4, which is a follow-on mission of ALOS-2. To implement land deformation and subsidence monitoring with SAR interferometry and to improve performance of disaster monitoring (more frequent, wider coverage), ALOS-4 observe wider swath while keeping as higher spatial resolution as that of ALOS-2. PALSAR-3, which is successor of PALSAR-2 onbaard ALOS-2, is now designed as an active phased array antenna with onboard digital beam forming processor in order to improve swath width of 3 m resolution Stripmap mode to 200 km.

Insufficient validation for polarimetric decomposition techniques has led to its less perceived popularity for more than 20 years. The true composition ratio of scattering mechanisms within a radar backscatter should be essential to make polarimetric synthetic aperture radar (SAR) operational applications. To achieve this, a novel comprehensive approach to accurately identify the contribution of each scattering mechanism by a multi-incidence angle and multipolarimetric (MIMP) SAR observation combined with a theoretical model simulation is newly applied to L-band SAR data. Rice paddies in Niigata, Japan having a simple vegetation structure without topography were observed by Polarimetric and Interferometric Airborne SAR L-band 2, by gradually varying the flight path in terms of incidence angle. In addition to the MIMP SAR observation, a dominant scattering mechanism is reliably isolated through the theoretical characterization of the data by a discrete scatterer model. Avoiding unnecessary Bragg scattering effect caused by the methodically distributed rice paddies, the volume scattering from grains is identified as a dominant scattering mechanism over incidence angles. In addition, HH and VV are strongly affected by the double-bounce scattering between stalks and the ground surfaces at only small incidence angles, whereas the contribution of the double-bounce scattering for HV is not obvious. The results at the L-band will be compared with another MIMP SAR data at the X-band obtained for the same rice paddies in 2014 so that multifrequency MIMP SAR data analysis shall be conducted for our next step.

SAR remote sensing has been expected as an efficient rice crop monitoring tool due to its flexible operability with wide swath. Though many researchers have intensively analysed the backscatter, there still exist non-negligible mysteries to extract physical parameters. To solve the problems, it is essential to conduct theoretical characterization of experimentally obtained radar backscatter from rice paddies. In this paper, rice paddies in Niigata city, Japan, were observed by the second version of L-band Polarimetric and Interferometric Airborne SAR (Pi-SAR-L2), by systematically varying the observation parameters such as incidence angle and polarization. Bragg scattering was obvious in the SAR imagery at L-band, and it was theoretically modeled. Then, backscatter from the rice paddy suppressing the Bragg scattering effect was obtained by utilizing the model.

SAR Tomography, or TomoSAR, is one of the powerful SAR data analysis tool with multi-baseline data set, especially for height estimation of scattering centers. On the other hand, PolSAR, or Polarimetric SAR, is also powerful tool for scattering characteristic discrimination of terrain. Several PolSAR analyses have applied to the TomoSAR. However the application of model-based PolSAR scattering power decomposition techniques have not reported yet. The model-based technique has superior performance to discriminate scattering property of local scatterers, hence it will assist to enhance resolution performance of TomoSAR with a limited multi-baseline dataset. In this paper, we show a basic concept of the model-based PolSAR scattering component decomposition for TomoSAR, and demonstrate it by using the multi-baseline dataset obtained by the PiSAR2-X.

Various polarimetric decomposition techniques have been proposed and demonstrated for more than 20 years. However, they still have never been operational applications because of insufficient validations. The true composition ratio of scattering mechanisms within a radar backscatter has to be known. To achieve this, a novel comprehensive approach to accurately identify the contribution of each scattering mechanism by a multiincidence angle and multipolarimetric (MIMP) synthetic aperture radar (SAR) observation combined with a theoretical model simulation is introduced. Rice paddies in Niigata City in Japan having a simple vegetation structure without topography were observed by X-band polarimetric and interferometric SAR 2 (Pi-SAR2), by gradually varying the flight path in terms of incidence angle. In addition to the MIMP SAR observation, a dominant scattering mechanism is reliably isolated through the theoretical characterization of the data by a discrete scatterer model. In the case of the rice paddies at a late vegetative stage, a dominant scattering on HH gradually varies from the double-bounce scattering to the volume scattering of rice grains in terms of incidence angle, whereas VV is affected by various volume scatterings of grains, leaves, and stems at the small, medium, and large incidence angles, respectively. HV is simply affected by the volume scattering of grains at the small incidence angle, and then it gradually switches to the volume scattering of the stalks. This brand new approach by the MIMP SAR observation with the theoretical modeling for this specific stage could take forward the polarimetric decomposition studies.

A sensitivity of L-band SAR to an inundated area was examined for an application of rapid flood mapping. Based on a theoretical scattering model, an intensive comparison by using the various real spaceborne and airborne L-band SAR data obtained by ALOS-2 was conducted. A proposed time series approach plays an important role to improve an accuracy of flooding area extraction.

SAR remote sensing has been expected as an efficient rice crop monitoring tool due to its flexible operability with wide swath. Though many researchers have intensively analysed the backscatter, there still exist non-negligible mysteries to extract physical parameters. To solve the problems, it is essential to conduct theoretical characterization of experimentally obtained radar backscatter from rice paddies. In this paper, the rice paddies were observed by a high resolution X-band SAR, Polarimetric and Interferometric SAR 2 (Pi-SAR2), by systematically varying the observation parameters such as incidence angle and polarization. The obtained features based on many sample points in Niigata city indicate that the dominant scattering mechanisms are gradually varied in terms of incidence angles.

Scattering component decomposition techniques for POLSAR data are powerful tool for the POLSAR data analysis. The three component decomposition technique is the one of the major technique among them. However, the algorithm requires several assumptions to solve the problem because of the lack of independent observables. To overcome this difficulty, we have proposed the multi-baseline POLSAR scattering component decomposition technique which utilizes the concept of the MUSIC algorithm with spatial smoothing preprocessing (SSP) scheme. However resolution performance depends on the decorrelation performance by the SSP. In this report, we propose several enhanced schemes for the SSP, and show some experimental results to show their performance.

In this paper, a visibility of area damaged by landslide on deeply forested mountain in Japan to an L-band SAR image was examined. The intensive comparison by using the various ALOS-1 and 2 data explained that the visibility is highly sensitive to the observation conditions such as an azimuthal angle. In addition, it was revealed that a simple linear combination of a full polarimetric data set can significantly improve the contrast of damaged area to the coniferous woodland. This is promising for a future development of effective landslide area detection in the forested mountain.

Current motion compensation algorithms for a moving object on synthetic aperture radar (SAR) imagery by refocusing are accurate, but they suffer from a severe computational efficiency problem. If the step size of an assumed target velocity is too large, one could miss the true target velocity, whereas too small a step size would lead to a considerable computation load. To address this issue, the way motion error affects a compressed signal should be understood. In this paper, we introduce the concept of velocity correlation function (VCF) to describe the sensitivity of an SAR compressed signal to motion error. We propose its analytical model by straightforwardly tackling conventional matched filtering, and then we thoroughly compare the model with the experimentally obtained VCF for each static and moving object on real L-band airborne/spaceborne SAR imagery. The analytically derived VCF is well validated, as it shows excellent agreement with those experiments. Finally, the VCF is generalized to a velocity correlation map to visualize a sensitivity of SAR system parameters to target motion in both the range and the azimuth directions simultaneously.

SAR polarimetry has been expected to be a potential candidate of a tool to measure physical parameters such as biomass and soil moisture. However, it still has some difficulties to even simply relate the parameters to a radar backscattering cross section. In this paper, a multi-incidence angle approach is applied to understand the radar backscatter from the vegetated field. We theoretically developed a boreal forest model based on Discrete scatterer model, and utilized it to simulate a backscatter in terms of incidence angles in some cases. Analyzing the simulation results, we revealed that a trunk in the boreal forest works as a radio wave absorber. In addition, increase and decrease of the backscatter is strongly influenced by the dominant scattering mechanism as expected. This theoretical study will be validated by real data which will be obtained by real airborne and spaceborne SAR sensors.

Polarimetric SAR decomposition has been expected to be a potential candidate of a tool to measure physical parameters of vegetated fields. For this context, it is important to validate theoretical model quantitatively with well controlled experiment. In this paper, simple boreal forest was modeled based on Discrete scatterer model. Through sensitivity study by using the model, we propose several useful indexes to infer vegetation parameters such as soil moisture. This study will be validated by X-band indoor full polarimetric SAR in Niigata University.

Polarimetric SAR decomposition has been used extensively because of its capability to identify the strength of each scattering mechanism from polarimetric SAR data. To achieve higher accuracy, one should take into account not only variation of vegetated terrain but also topography under vegetation. In this paper, we firstly isolate vegetation orientation angle from generalized volume scattering component, and then modify a conventional adaptive model-based decomposition by carefully introducing a concept of topographic polarization orientation compensation (TPOC). Finally, experimental validation is conducted by using P-band AIRSAR image of Black Forest in Germany.

The moisture content of terrain or forest, which is highly related to the permittivity, is a key parameter to understand global water cycle. This study is focused on retrieving permittivity from radar backscattering measurements. The basic concept of the proposal is to utilize Brewster's angle effect of double-bounce backscattering. If the Brewster's angle is known, it is possible to estimate the permittivity of medium directly. In order to demonstrate the Brewster's angle effects on the backscattering of forested area, the authors have carried out a simple laboratory experiment and the results are shown in this paper.

The needs for SAR remote sensing has been rapidly increased to efficiently promote ocean security. In this context, solid ship detection algorithm plays a vital role to execute expected performance. Most of conventional algorithms utilize a Constant False Alarm Rate (CFAR) technique, and have achieved great success. However, there has been no solid procedure to select an appropriate CFAR threshold for various area. In this paper, based on physical scattering theory we proposed a brand-new Standard Deviation (STD) filter by which a dependency to CFAR threshold can be significantly decreased. Validation of the technique was also conducted using Pi-SAR image provided by Japan Aerospace Exploration Agency (JAXA).

Adaptive model-based polarimetric SAR decomposition has attracted significant attention to estimate the vegetation parameters of various vegetated terrains. However, an increased flexibility of parameter selection potentially causes unexpected misinterpretation. Using reasonable constraints, we should validate the decomposition results thoroughly based on solid physical interpretation. In this paper, we apply most general adaptive model-based decomposition technique to real SAR data by varying various vegetation parameters. Then validation will be conducted based on physical scattering theory.

Previous model-based decomposition techniques are applicable to a limited range of vegetation types because of their specific assumptions about the volume scattering component. Furthermore, most of these techniques use the same model, or just a few models, to characterize the volume scattering component in the decomposition for all pixels in an image. In this paper, we extend the model-based decomposition idea by creating an adaptive model-based decomposition technique, allowing us to estimate both the mean orientation angle and a degree of randomness for the canopy scattering for each pixel in an image. No scattering reflection symmetry assumption is required to determine the volume contribution. We examined the usefulness of the proposed decomposition technique by decomposing the covariance matrix using the National Aeronautics and Space Administration/Jet Propulsion Laboratory Airborne Synthetic Aperture Radar data at the C-, L-, and P-bands. The randomness and mean orientation angle maps generated using our adaptive decomposition significantly improve the physical interpretation of the scattering observed at the three different frequencies.

Model-based decomposition of polarimetric radar covariance matrices holds the promise that specific scattering mechanisms can be isolated for further quantitative analysis. In this paper, we show that current algorithms suffer from a fatal flaw in that some of the scattering components result in negative powers. We propose a simple modification that ensures that all covariance matrices in the decomposition will have nonnegative eigenvalues. We further combine our nonnegative eigenvalue decomposition with eigenvector decomposition to remove additional assumptions that have to be made before the current algorithms can be used to estimate all the scattering components. Our results are illustrated using Airborne Synthetic Aperture Radar data and show that current algorithms typically overestimate the canopy scattering contribution by 10%-20%.

Soil moisture inversion from polarimetric SAR data has attracted significant attention for the past twenty years. Comparing with the simple case of bare surface, it is extremely complicated for vegetated terrain to invert soil moisture because of a larger number of scattering mechanisms that contributes to the observation. In this paper, we show how polarimetric decomposition technique, which decomposes SAR observations into preferred scattering mechanisms, can be used for the inversion. The result leads us to give up the use of polarimetric decomposition because of an unknown attenuation ratio caused by the canopy. Then a new inversion algorithm using Polarimetric Scattering Cubes (PSC) is introduced with simulation results to show a sensitivity to physical parameter such as vegetation distribution. Finally, we also discuss how the technique should be implemented for the real SAR data.

Current polarimetric model-based decomposition techniques are limited to specific types of vegetation because of their assumptions about the volume scattering component. In this paper, we propose a generalized probability density function based on thenth power of a cosine-squared function. This distribution is completely characterized by two parameters; a mean orientation angle and the power of the cosine-squared function. We show that the underlying randomness of the distribution is only a function of the power of the cosine-squared function. We then derive the average covariance matrix for various different elementary scatterers showing that the result has a very simple analytical form suitable for use in model-based decomposition schemes.

Several authors have proposed decompositions of measured polarimetric covariance matrices into the weighted sums of individual covariance matrices that represent known scattering models. In this paper we critically examine some minimum requirements that these model-based polarimetric scattering decompositions must satisfy. Starting with the definition of the covariance matrix, we show that a necessary requirement for these decompositions is that the eigenvalues of the individual covariance matrices must be non-negative. We then show with examples that this is not guaranteed for some of the proposed decompositions. Finally, we propose a method of decomposition that would ensure that the eigenvalues of the individual covariance matrices would be non-negative.