Identifikasi Genangan Banjir di Wilayah DKI Jakarta Menggunakan Citra Satelit Sentinel-1

Provinsi DKI Jakarta merupakan wilayah dengan karakteristik topografi yang berada pada kemiringan lereng relatif landai dan terletak pada dataran rendah. Hal ini menyebabkan wilayah Jakarta menjadi semakin rentan tergenang air dan banjir pada musim hujan. Pada awal tahun 2020, Jakarta mengalami banjir yang diakibatkan oleh hujan lebat yang turun sejak 31 Desember 2019 hingga 1 Januari 2020. Untuk keperluan antisipasi dan mitigasi bencana terkait banjir tersebut diperlukan ketersediaan data tentang luas genangan banjir dan letaknya secara cepat. Salah satu teknologi yang potensial untuk diterapkan adalah menggunakan citra satelit Sentinel-1. Berdasarkan kondisi tersebut, telah dilakukan kajian untuk mendeteksi daerah terdampak genangan banjir dari citra satelit Sentinel-1. Adapun teknik yang digunakan adalah metode change detection dan thresholding. Pada kajian ini digunakan citra Sentinel-1 perekaman tahun 2019 dan 2020, DEMNAS, Global Surface Water dan batas administrasi wilayah DKI Jakarta. Adapun tahapan pelaksanaan kajian mencakup proses change detection dengan ratio image dari dua citra Sentinel-1 GRD sebelum dan saat banjir, perhitungan nilai optimum threshold untuk menentukan banjir dan tidak banjir, masking banjir, dan perhitungan luas genangan. Berdasarkan hasil kajian diperoleh luas total daerah yang terkena banjir sekitar 1.156,84 hektar, sedangkan luas area banjir dari data Pemprov DKI sekitar 12.896,35 hektar. Hasil validasi menggunakan data Pemprov DKI dengan membandingan antara citra Sentinel-1 tahun 2019 dan 2020, diperoleh hasil 61 lokasi atau sekitar 28,96% termasuk ke dalam hasil interpretasi kelas banjir dan terdapat 157 lokasi atau sekitar 71,04% termasuk ke dalam hasil interpretasi kelas tidak banjir.

Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images

We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented—stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our previously achieved results. Significant improvements are observed as well as the capacity to reliably extract physically meaningful information from measurements performed under highly adverse imaging conditions. The showcased image processing solution and separate elements thereof are readily extendable beyond the present application, and have been made open-source.

Fast and flexible processing of large FRET image stacks using the FRET-IBRA toolkit

Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. The FRET-IBRA package has been extensively used in quantifying the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Comparisons with existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.

Applying MRI Intensity Normalization on Non-Bone Tissues to Facilitate Pseudo-CT Synthesis from MRI

This study aimed to facilitate pseudo-CT synthesis from MRI by normalizing MRI intensity of the same tissue type to a similar intensity level. MRI intensity normalization was conducted through dividing MRI by a shading map, which is a smoothed ratio image between MRI and a three-intensity mask. Regarding pseudo-CT synthesis from MRI, a conversion model based on a three-layer convolutional neural network was trained and validated. Before MRI intensity normalization, the mean value ± standard deviation of fat tissue in 0.35 T chest MRI was 297 ± 73 (coefficient of variation (CV) = 24.58%), which was 533 ± 91 (CV = 17.07%) in 1.5 T abdominal MRI. The corresponding results were 149 ± 32 (CV = 21.48%) and 148 ± 28 (CV = 18.92%) after intensity normalization. With regards to pseudo-CT synthesis from MRI, the differences in mean values between pseudo-CT and real CT were 3, 15, and 12 HU for soft tissue, fat, and lung/air in 0.35 T chest imaging, respectively, while the corresponding results were 3, 14, and 15 HU in 1.5 T abdominal imaging. Overall, the proposed workflow is reliable in pseudo-CT synthesis from MRI and is more practicable in clinical routine practice compared with deep learning methods, which demand a high level of resources for building a conversion model.

Statistical properties of an unassisted image quality index for SAR imagery

© 2019 by the authors. The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich theMestimator to extend its capabilities.

Statistical properties of an unassisted image quality index for SAR imagery

© 2019 by the authors. The M estimator is a recently proposed image-quality index used to evaluate the despeckling operation in SAR (Synthetic Aperture Radar) data. It is used also to rank despeckling filters and to improve their design. As a difference with traditional image-quality estimators, it operates not on the filtered result but on a derived one, i.e., the ratio image. However, a deep statistical analysis of its properties remains open and, with it, the ability to use it as a test statistic. In this work, we focus on obtaining insights into its distribution as well as on exploring other remarkable statistical properties of this unassisted estimator. This study is performed through EDA (Exploratory Data Analysis) and the well-known ANOVA (ANalysis Of VAriance). We test our results on a set of simulated SAR data and provide guides to enrich theMestimator to extend its capabilities.

Possibility for Identifying/Extracting Rock Outcrop Using Landsat 8 OLI/TIRS - Case Study of Thua Thien Hue Province

This paper approaches the ratio image method to extract the exposed rock information from the Landsat 8 OLI/TIRS satellite image (2019) according to the object orientation classification. Combining automatic interpretation and interpretation through threshold of image index values according to interpretation key the object orientation classification to separate soil object containing exposed rock and no exposed rock in Thua Thien Hue province. Using the Topsoil Grain Size Index (TGSI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI) and other related analytical problems have identified 40 exposed rock storage areas in the study area. The results have been verified in the field and the Kappa index is 85.10%.

Detecting the Pedestrian Shed and Walking Route Environment of Urban Parks with Open-Source Data: A Case Study in Nanjing, China

The propensity for visiting urban parks is affected by the park’s attractiveness and travel convenience, where walking provides the most basic and fair access. Walking routes from residences to parks, in terms of duration and perception, have received insufficient attention in the literature, particularly in the urban form context in China. Using the case study of Xuanwu Lake Park in Nanjing, we acquire walking routes from residences to the park through open-source data scraping in order to depict the pedestrian shed and pedestrian environment reasonably along these routes. The results show that the walking routes vary significantly with regards to distance, turns, street views, and so on. Proximity to urban parks, in terms of Euclidean distance, does not necessarily correspond to actual route distance, which may have a more direct influence on travel convenience and, hence, visiting propensity. Palpable differences in green visual ratio, image elements, and points of interest along these routes may also contribute to pedestrian environmental disparity. Analyzing data obtained from an online map provides a rapid and objective approach to detect pedestrian sheds and diagnose pedestrian environments, which can facilitate urban planners and policy makers in siting new parks and assessing the service capacity of parks.