Development of a new dust index NDLI for Asian dust extraction system based on Aqua MODIS data and monitoring of trans-boundary Asian dust events in Japan

Heavy Asian dust events occur due to the strong wind in the Gobi deserts and are occasionally carried to Korea, Japan, and North America. They cause problems in human lives, such as respiratory diseases, transportation disturbances due to reduce visibility, and other disruptions in social activities. Remote sensing technology is useful for detecting and monitoring such airborne dust and understanding the distributions and movements of dust. To understand the Asian dust events, in this study, a new dust index is developed for the efficient detection of airborne Asian dust, which is a composite of two Moderate Resolution Imaging Spectroradiometer (MODIS) indices: Brightness Temperature Difference (BTD) and Normalized Difference Dust Index (NDDI). Our proposed Normalized Dust Layer Index (NDLI) detects dust more efficiently. To identify the characteristics of annual Asian dust events in Japan, a statistical time-series analysis of data from the years 2010, 2013 and 2014 is performed, and it is found that the dust events in 2014 were relatively calmer than those in 2013. An evaluation that was based on ground observations over different sites in Japan indicated that the proposed method performed well. Finally, we integrated our NDLI product into the trans-boundary air pollution satellite image database (TAPSIDB) system for monitoring Asian dust events.     

MODIS图像云检测业务化算法研究

本文针对3种不同空间分辨率的MODIS数据(250m,500m,1km),提出相应的业务化云检测算法,采用多阈值判别云像元,算法简单有效,仅以图像作为输入,不需要其它辅助数据的支持。以我国中低纬度地区为实验区,分别选取了春夏秋冬4幅MODIS图像,根据图像选择了合理的云判别阈值,取得了较好的云检测结果,与美国宇航局(NASA)提供的MODIS云产品相比,云的空间分布基本一致,且很好地更正了MODIS云产品中被误判为云区的河流、湖水、岸边的区域,从而可以获得更高的云检测精度和提高云覆盖判别的效率,为建立业务化运行的遥感图像云剔除预处理流程奠定基础。     

Sand and dust storm detection over desert regions in China with MODIS measurements**

A new approach is developed for quick detection of sand and dust storms (SDSs) over arid and semi-arid regions of the northwestern part of China, where the bright-reflecting source areas of Asian dust outbreaks are located. The Asian dust particles, once with proper conditions, can even transport across the Pacific Ocean and reach the USA and Canada. Remote-sensing data products of mineral dust near its source are deficient because of the radiance contributions of the bright surface. In this article, based on Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, consecutive separation of dust cloud from bright underlying surface and water/ice cloud is completed by utilizing a refined cloud mask algorithm and the normalized difference dust index (NDDI). Thresholds are determined through statistical analysis of MODIS measurements over the Taklimakan and Gobi deserts. Validations with ground observations over the sites in Inner Mongolia and Xinjiang in China demonstrated good performance of the proposed method in separating SDS from bright surface and cloud.     

煤矿井下雾尘图像清晰化算法

针对由于煤矿井下环境存在大量煤尘、水雾,监控图像出现模糊、退化现象的问题,提出一种基于暗原色原理和主成分分析的煤矿井下雾尘图像清晰化算法。该算法基于大气散射模型,根据暗原色原理计算透射率;用主成分分析法得出能够充分反映雾尘图像信息的亮度、饱和度及对比度,通过对这些指标进行加权处理来计算大气光值,实现了对煤矿井下雾尘图像的清晰化处理。仿真结果表明,该算法可较大程度地还原图像细节,并保持图像的真实性和结构完整性,实时性较好。     

An enhanced concurrency control scheme for multidimensional index structures

We propose an enhanced concurrency control algorithm that maximizes the concurrency of multidimensional index structures. The factors that deteriorate the concurrency of index structures are node splits and minimum bounding region (MBR) updates in multidimensional index structures. The properties of our concurrency control algorithm are as follows: First, to increase the concurrency by avoiding lock coupling during MBR updates, we propose the PLC (partial lock coupling) technique. Second, a new MBR update method is proposed. It allows searchers to access nodes where MBR updates are being performed. Finally, our algorithm holds exclusive latches not during whole split time but only during physical node split time that occupies the small part of a whole split process. For performance evaluation, we implement the proposed concurrency control algorithm and one of the existing link technique-based algorithms on MIDAS-III that is a storage system of a BADA-IV DBMS. We show through various experiments that our proposed algorithm outperforms the existing algorithm in terms of throughput and response time. Also, we propose a recovery protocol for our proposed concurrency control algorithm. The recovery protocol is designed to assure high concurrency and fast recovery.     

Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East

Dust storms have a major impact on air quality, economic loss, and human health over large regions of the Middle East. Because of the broad extent of dust storms and also political–security issues in this region, satellite data are an important source of dust detection and mapping. The aim of this study was to compare and evaluate the performance of five main dust detection algorithms, including Ackerman, Miller, normalized difference dust index (NDDI), Roskovensky and Liou, and thermal-infrared dust index (TDI), using MODIS Level 1B and also MODIS Deep Blue AOD and OMI AI products in two dust events originating from Iraq and Saudi Arabia. Overall, results showed that the performance of the algorithms varied from event to event and it was not possible to use the published dust/no-dust thresholds for the algorithms tested in the study area. The MODIS AOD and OMI AI products were very effective for initial dust detection and the AOD and AI images correlated highly with the dust images at provincial scale (p-value <0.001), but the application of these products was limited at local scale due to their poor spatial resolution. Results also indicated that algorithms based on MODIS thermal infrared (TIR) bands or a combination of TIR and reflectance bands were better indicators of dust than reflectance-based ones. Among the TIR- based algorithms, TDI performed the best over water surfaces and dust sources, and accounted for approximately 93% and 90% of variations in the AOD and OMI AI data.     

Nighttime polar cloud detection with MODIS

Cloud detection is the first step in studying the role of polar clouds in the global climate system with satellite data. In this paper, the cloud detection algorithm for the Moderate Resolution Imaging Spectrometer (MODIS) is evaluated with model simulations and satellite data collocated with radar/lidar observations at three Arctic and Antarctic stations. Results show that the current MODIS cloud mask algorithm performs well in polar regions during the day but does not detect more than 40% of the cloud cover over the validation sights at night. Two new cloud tests utilizing the 7.2 μm water vapor and 14.2 μm carbon dioxide bands, one new clear-sky test using the 7.2 μm band, and changes to the thresholds of several other tests are described. With the new cloud detection procedure, the misidentification of cloud as clear decreases from 44.2% to 16.3% at the two Arctic stations, and from 19.8% to 2.7% at the Antarctic station.     

A new shadow detection index for 8-band very high-resolution satellite images

ABSTRACT

The new generation of remote sensing satellite with very high-resolution images has provided a high level of details, which make them a reliable source of information. Presence of shadow can reduce the amount of information that can be extracted from these images. Shadow can be confused with dark objects such as water and dark vegetation. The main aim of this research is to develop a new index to detect shadow in the presence of dark objects using the capabilities of the new remote sensing satellite images. For this study, WorldView-2 (WV-2) remote sensing satellite images with eight spectral bands were used. A spectral reflectance analysis for the main ground features has been studied along the eight spectral bands to determine the most effective bands for shadow detection. These bands are employed with the Hue-Saturation-Intensity colour model for producing the new proposed Saturation Intensity Shadow Detection Index (SISDI). The proposed index is applied to four study areas and compared with two state-of-the-art indices of shadow detection. Results of this comparison demonstrate the more accuracy effectiveness and feasibility of that proposed index. The proposed index achieves the highest overall accuracy (average of 97.8%) and has the ability for detecting small shadow areas.     

Blending MODIS and Landsat images for urban flood mapping

Satellite images provide important data sources for monitoring flood disasters. However, the trade-off between spatial and temporal resolutions of current satellite sensors limits their uses in urban flooding studies. This study applied and compared two data fusion models, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), in generating synthetic flooding images with improved temporal and spatial resolution for flood mapping. The synthetic images are produced in two scenarios: (1) for real-time prediction based on Landsat and MODIS images acquired before the investigated flooding; and (2) for post-disaster prediction based on images acquired after the flooding. The 2005 Hurricane Katrina in New Orleans was selected as a case study. The result shows that the Landsat-like images generated can be successfully applied in flood mapping. Particularly, ESTARFM surpasses STARFM in predicting surface reflectance in both real-time and post-flooding predictions. However, the flood mapping results from the Landsat-like images produced by both STARFM and ESTARFM are similar with overall accuracy around 0.9. Only for the flooding maps of real-time predictions does ESTARFM get a slightly higher overall accuracy than STARFM, indicating that the lower quality of the Landsat-like image generated by STARFM may not affect flood mapping accuracy, due to the marked contrast between land and water. This study suggests great potential of both STARFM and ESTARFM in urban flooding research. Blending multi-sources images could also support other disaster studies that require remotely sensed data with both high spatial and temporal resolution.     

面向彩色增强图像的客观质量评价算法

目的 现有的全参考图像质量评价方法使用“完美”的源信号作为参考,但是增强图像的参考图像通常不是“完美”的.因此,现有的全参考质量评价方法不能用于增强图像的评价,提出了一种新的面向彩色增强图像的质量评价算法.方法 利用图像的梯度、颜色和亮度特征,提出了增强图像的梯度增强图、颜色增强图和亮度增强因子的计算方法,计算增强图像相对于参考图像在梯度、颜色和亮度方面的增强程度;并建立了亮度增强因子和梯度增强图、颜色增强图之间的关系模型;另外,原图像的梯度和颜色特征也被提取用于增强图像的质量评价.结果 使用公开数据库进行的实验结果表明,该算法和现有最优算法相比,皮尔逊线性相关系数(PLCC)和斯皮尔曼相关系数(SROCC)分别提高了2.9%和2.5%,而均方根误差(RMSE)则降低了12.3%,获得了比现有算法更优越的性能.结论 本文算法解决了目前已有的评价算法需要参考图像为“完美”图像,而且增强图像质量无法采用相似性程度进行计算的问题,适用于为了获得更好视觉质量的不含噪增强图像的质量评价.     

An efficiency enhanced cluster expanding block algorithm for copy-move forgery detection

Multimedia Tools and Applications - The proposed scheme detects the copy-move forgery detection regions through the invariant features extracted from each block. First, an image is divided into...     

An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images

Multimedia Tools and Applications - Diabetic Retinopathy (DR) is one of the long-lasting Diabetic retinal disorders that leads to vision impairment eventually blindness in most of the working-age...     

基于强化底层特征的无人机航拍图像小目标检测算法

针对无人机航拍图像小目标检测整体精度低、漏检误检的问题,提出了一种新的基于强化底层特征的多尺度小目标检测方法。该方法以Faster R-CNN-ResNet-50-FPN为基础模型,首先,设计提出了新的DetNet-59特征提取网络;其次,设计了扁平的Flat-FPN特征融合网络来提高强化底层特征;最后通过引入soft-NMS解决小目标重叠问题。所提出的算法在VOC2007和VisDrone2019数据集上进行仿真实验测试,在时间消耗提升不大于2%的情况下,mAP较基础模型提高了约11%,并且检测精度也优于现阶段的常用算法。实验结果表明,该算法在保证实时性的同时可以有效提高小目标检测精度。     

声纳图像处理中的连通成分标记算法研究

在高频声纳图像目标检测中,对图像分割后,需要对前景目标参数进行提取。水下声学图像相对于光学图像而言,分辨率相对较低,并且通常不会有特别复杂的图形图案进行处理。本文根据声学图像的特点,提出了一种连通成分标记算法,利用此算法可以对分割后的声纳目标进行标记,并快速提取出声图中的目标个数,以及各个目标的位置、面积等特征参数。此算法在VC++软件平台上对扫描声纳图像进行了实时处理,结果验证了该算法的可行性。     

An adaptive fuzzy-genetic algorithm approach for building detection using high-resolution satellite images

We propose a new approach for building detection using high-resolution satellite imagery based on an adaptive fuzzy-genetic algorithm. This novel approach improves object detection accuracy by reducing the premature convergence problem encountered when using genetic algorithms. We integrate the fundamental image processing operators with genetic algorithm concepts such as population, chromosome, gene, crossover and mutation. To initiate the approach, training samples are selected that represent the specified two feature classes, in this case “building” and “non-building”. The image processing operations are carried out on a chromosome-by-chromosome basis to reveal the attribute planes. These planes are then reduced to one hyperplane that is optimal for discriminating between the specified feature classes. For each chromosome, the fitness values are calculated through the analysis of detection and mis-detection rates. This analysis is followed by genetic algorithm operations such as selection, crossover and mutation. At the end of each generation cycle, the adaptive-fuzzy module determines the new (adjusted) probabilities of crossover and mutation. This evolutionary process repeats until a specified number of generations has been reached. To enhance the detected building patches, morphological image processing operations are applied. The approach was tested on ten different test scenes of the Batikent district of the city of Ankara, Turkey using 1 m resolution pan-sharpened IKONOS imagery. The kappa statistics computed for the proposed adaptive fuzzy-genetic algorithm approach were between 0.55 and 0.88. The extraction performance of the algorithm was better for urban and suburban buildings than for buildings in rural test scenes.     

MODIS satellite data evaluation for sand and dust storm monitoring in Saudi Arabia

ABSTRACT

The impacts of wind-blown desert sand and dust are a major concern of environmental and climate study due to their global extent. This article investigates the sand and dust storms detection in Saudi Arabia using Moderate Resolution Imaging Spectroradiometer (MODIS) data, both from Terra and Aqua satellite systems for the years 2002–2011. Normalized Difference Dust Index (NDDI) is applied for the detection of sand and dust storms whilst MODIS band 31 is applied to discriminate atmospheric sand and dust from that present on the ground. In addition, the data from Meteosat satellite, AERONET station, and meteorological stations are used to validate NDDI-based sand and dust storm events. The results of the study show that NDDI can successfully identify and differentiate sand and dust storms from clouds whilst MODIS band 31 can discriminate aerial and surface sand and dust over Saudi Arabia. The results also show that the multi-source data, that is MODIS, Meteosat, AERONET, and meteorological stations, can be very valuable for tracking sand and dust storm events. As no such attempt in the past has been made in Saudi Arabia, it is envisaged that the results of this study will be helpful in planning remote-sensing data for the climate change study in the region.     

An enhanced text detection technique for the visually impaired to read text

An enhanced text detection technique (ETDT) is proposed, which is expected to aid the visually impaired to overcome their reading challenges. This work enhances the edge-preserving maximally stable extremal regions (eMSER) algorithm using the pyramid histogram of oriented gradients (PHOG). Histogram of oriented gradients (HOG) derived from different pyramid levels is important while detecting maximally stable extremal regions (MSER) in the ETDT approach because it gives more spatial information when compared to HOG information from a single level. To group text, a four-line, text-grouping method is newly designed for this work. Also, a new text feature, Shapeness Score is proposed, which significantly identifies text regions when combined with the other features based on morphology and stroke widths. Using the feature vector of dimension 10, the J48 decision tree and AdaBoost machine learning algorithms identify the text regions in the images. The algorithm yields better results than the existing benchmark algorithms for the ICDAR 2011 born-digital dataset and must be improved with respect to the scene text dataset.     

图像脉冲噪声检测

提出了一个包含两个自适应神经模糊推理系统和一个后处理块的网络,该网络可用于灰度图像脉冲噪声检测。网络中每个自适应神经模糊推理系统都是一个四输入单输出一阶Sugeno模糊推理系统。所提出的脉冲噪声检测方法分两步进行：对该网络进行优化训练,确定其参数;用优化后的网络对被椒盐脉冲噪声污染的图像进行噪声检测。实验结果表明,与其他传统检测方法相比,所提出的方法,更能有效检测出图像中椒盐脉冲噪声。