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It is often uncomfortable for disabled individuals, especially those with vision impairment, to conduct educational activities in collaboration with people that have perfect vision. This can be because of the former's lack of confidence, vision capability, and acceptance. Information and communications technology (ICT) has played a vital role in giving support to people with visual impairments so that they can overcome their issues. This study proposes innovative solutions that address the challenges faced by partially or completely visually impaired people. It provides an interactive and intelligent interface, which they may use to perform educational activities, such as editing, writing, or reviewing documents, in collaboration with people without visual impairments. The system provides high-quality awareness features by sending them instant voice notifications about the actions and events occurring in the shared environment. A speech-recognition engine has been integrated into the system to allow users to interact with the application through voice commands. The system is evaluated through experiments, where people with visual impairment and people without visual impairment were engaged in collaborative writing. The obtained results are encouraging. The users showed curiosity in the system and were able to focus on the productive task instead of their disability. [ABSTRACT FROM AUTHOR]

A novel unsupervised image classification algorithm which based on the sparse representation theory for polarimetric synthetic aperture radar (PolSAR) image is introduced in this paper. The algorithm conjunctively uses sparse representation-based classification (SRC) theory, dictionary updating method, and label smoothness constraint to update class labels. The unsupervised H/ /A Wishart classification method is introduced to provide the preliminary classification result, from which the initial dictionary and class labels can be extracted. An energy function is defined, and it contains two terms. The first term is based on the sparse representation theory. It reflects the cost of assigning different class labels to a pixel. The second term is label smoothness constraint. It constrains that class labels of neighbouring pixels in flat regions should be the same. By alternately minimizing the energy function, two unknown variables, dictionary and class labels are updated. Optimized class labels are the outputs to compose the final classification result. Extensive experimental results for three PolSAR datasets are analysed to verify the validity of the proposed method. Comparison with other unsupervised/supervised classification methods indicates its superiority. [ABSTRACT FROM AUTHOR]

The concern about the recurrence of land subsidence has arisen in Japan with urban development and new groundwater use. Land subsidence observation well has been utilized for monitoring the subsidence, but due to the recent aging of facilities and equipment, remote sensing technology is attracting attention. This paper seeks to assess the substitutability of Consecutive DInSAR with the land subsidence observation well. To investigate the subsidence in Kanagawa prefecture, Sentinel 1 images from August 2017 to March 2022 were analyzed by SARPROZ software based on Consecutive DInSAR, Global Navigation Satellite System (GNSS), and land subsidence observation well methods. Moreover, we assessed the new calculation model between land surface pressure and ground water called the law of material conservation to estimate land subsidence. The results revealed that the Consecutive DInSAR results agreed with the land subsidence observation well data, indicating the possibility of future substitution. [ABSTRACT FROM AUTHOR]

In this study, ALOS-2 PALSAR L-band dual-pol (HH and HV) synthetic aperture radar (SAR) images were used for above ground biomass (AGB) estimation of Shisham (Dalbergia sissoo) tree species in the managed forest of Chichawatni Irrigated Plantation, Sahiwal District, Punjab, Pakistan. A total of 15 plots were surveyed during the field campaign and locally developed tree species-specific allometric equation was used for AGB calculation. The SAR images were pre-processed and calibrated to sigma nought (dB) for statistical AGB modeling. Nonlinear regression models between field-based AGB (ton/ha) and SAR backscatter sigma nought (dB) values giveR2(0.47 and 0.55) and RMSE (1.18 and 1.87) values for HH and HV-pol data, respectively. AGB maps generated through these regression models from both HH and HV-pol data show similar AGB values and spatial patterns. Due to a small number of sample plots, the Leave-One-Out Cross-Validation (LOOCV) proxy validation method was used for model validation, between estimated and predicted backscatter sigma nought (dB) values. The validationR2values are small; however, outliers are identifiable in the validation scatterplots. The AGB maps from this study are useful as a first estimate of AGB values from remote sensing in the study region, especially in the context of the REDD+ program. [ABSTRACT FROM PUBLISHER]

Silica is one of the mineral needs of industry, especially in base materials for the primary or auxiliary raw materials. Silica sand on Rupat Island was identified on the northern coastline. Selected samples were measured to get the percentage of minerals/elements using X-ray fluorescence. Two adjacent scenes of ALOS PALSAR full-polarimetry data were used. The dielectric constant of samples was measured using a dielectric probe kit in the frequency range from 0.3 to 3.0 GHz and also used for calculating the backscattering coefficient. Freeman-Durdeen and Yamaguchi techniques were used to obtain the scattering decomposition of physical scattering from the incoherent object model base. Surface scattering is a very clear decomposition used to show the silica sand identification compared with other decompositions. From surface scattering, the backscattering coefficient value of silica sand resulted from −59 dB to −52 dB. Thickness estimation from satellite and field measurements show the minimum is 59 cm and the maximum is 74 cm, with the average differentiation between these measurements being 4.88 cm. The condition supported by the grain size of silica sand particles that have almost the same size and shape were conducted by using microscopic photograph testing. These values were caused by a slightly rough surface. [ABSTRACT FROM PUBLISHER]

Analyses of single-post-event polarimetric synthetic aperture radar (PolSAR) data permit fast and convenient post-disaster damage assessment work. By analyzing valid features, damaged and undamaged buildings can be quickly classified. However, the presence of oriented buildings in the disaster area makes the classification work more challenging. Many previous works extract the damage information of the disaster area by considering oriented buildings and undamaged parallel buildings as survived buildings. However, after-effect debris may create structures with random orientation angles. In our study on the Tohoku earthquake/tsunami disaster event, we found that some damaged buildings with large building orientation angles (with respect to the satellite flight path) are grouped as oriented buildings (undamaged buildings). In this paper, we propose a new earthquake/tsunami damage assessment method, particularly for urban areas, that takes this complex situation into consideration. The proposed method solves the problems of both urban-area extraction and damaged-building identification. For urban-area extraction, the proposed combined thresholding and majority voting method can accurately discriminate between urban and foreshortening mountain areas. Meanwhile, for damaged-building identification, the proposed new unsupervised damage assessment method classifies the buildings in a disaster area according to four conditions, and it outperforms the techniques used in existing works. The analysis results and the comparison with the supervised support vector machine (SVM) classification technique show that our proposed method can produce more accurate results for damage assessment using single-post-event PolSAR data. [ABSTRACT FROM AUTHOR]