Pixel Comparison Research Articles

Wound Assessment in Pedobarography Using Image Segmentation Techniques

Pedobarography is elementary for kinetic gait analysis along with the analysis and exploration of multiple neurological and musculoskeletal diseases. One person among 11 adults suffer from Diabetes Mellitus. Also, Foot ulcers (FU) is a most harmful as well as associated chronic complications springing from diabetes mellitus (DM). Recently, there has been an evolving awareness that understanding the biomechanical factors beneath the diabetic ulcer in a better manner could result in improving the control activities over the disease, with considerable socio-economic effects. Diabetic Foot Ulcers (DFU) is a primary concern of this health issue, and if this is not addressed right can result in amputation. So in this research, the Image segmentation algorithms and Perimeter pixel comparison is carried out for wound classification depending on the simulation algorithm like Adaptive K-means, Clustering K means, Fuzzy C means, and Region growing approaches and among them, Fuzzy C means is found to achieve greatest accuracy of perimeter pixel values, which are 603, 462 and 356 pixel values in stages one, two and three. The time taken for execution among all the four simulation algorithms are observed and it can be revealed that Adaptive K means yields the least execution time for carrying out the simulation of foot ulcer. An evaluation on the self-assessment of wounds caused during diabetic foot ulcer employing image segmentation is developed. It is ultimately found that the objective of the image analysis pertaining to the ulcer in foot is the dynamic evaluation and definition of regions of high pressure in a diabetic patient’s foot depending on the estimations made on the perimeter pixel comparison and execution time.

Usporedba i analiza javno dostupnih batimetrijskih modela na području istočnog Jadranskog mora

In this paper the latest versions of six publicly available bathymetry models: DTU10bat, EMODnet 2018, ETOPO1, GEBCO 2020, Smith and Sandwell V20.1 and SRTM15+ V.2.1. are compared and evaluated in the area of continental shelf of Croatia settled along well intended east Adriatic coast. Survey data in the area is not available through open access data bases, but publicly accessible in agreement with data holders (hydrographic institute, research centres and industry). These grids provide alternative sources of information about seafloor topography. Marine researchers should be acquainted with the main characteristics as well as pros and cons of bathymetry models in order to choose the best one for a specific purpose. In this paper the most important characteristics and information about grids are presented: resolution, coverage, release date, horizontal and vertical datum, data source, registration method, producer and link to website with an emphasis on the underlying source data. The underlying source data is one of the most important parameters that determine the quality of the bathymetric model. Hypsometry curve that is describing the area distribution of depth is calculated for each bathymetry model over the test area of the east Adriatic. For pixel to pixel comparison, grids were resampled to same one-minute resolution and absolute differences between models are calculated in identical points. Absolute differences between models show level of mutual compatibility between models as well as areas of highest disagreements that indicate the presence of outliers or systematic errors within models. In order to demonstrate how well publicly available bathymetry models fit the true topography of the sea floor, grids were compared to high-resolution digital bathymetry model interpolated from the multibeam survey in the area of Murter Sea. This paper should assist in the choice of a most suited bathymetry grid in future maritime studies in the Adriatic.

Comparison of pixel- and object-based image analysis for tea plantation mapping using hyperspectral Gaofen-5 and synthetic aperture radar data

Accurately mapping tea plantation distribution is crucial to environmental protection and sustainable development. Hyperspectral and synthetic aperture radar (SAR) data have recently been widely used in land cover classification, but their ability to extract tea plantation regions still needs to be confirmed. Compared with traditional pixel-based image analysis (PBIA), object-based image analysis (OBIA) for tea plantation mapping is more worthy of implementation. This study explored the performance of Gaofen-5 (GF-5) and copolarized SAR data for tea plantation mapping using pixel- and object-based support vector machine algorithms in Wuyishan, China. Comparison of PBIA and OBIA demonstrated the significant differences in visual effect and classification accuracy. The object-based classifications especially offered a more contiguous depiction with fewer speckles of tea plantations than pixel-based classifications did. Moreover, object-based classifications improved overall accuracy (OA) between 1.7% and 7.9% in all scenarios when compared to pixel-based classifications. As for datasets, classifications using only GF-5 data obtained an OA of over 85%, while fusing images decreased classification accuracy due to the lower separability between tea and forest, showcasing that the fusion of hyperspectral and SAR data does not guarantee the improvement of classification accuracy. The integration of GF-5, horizontal transmit and horizontal receive (HH), and vertical transmit and vertical receive (VV) polarized data outperformed other data combinations in both pixel- and object-based classifications and achieved the highest OA (95.58%) in OBIA, with a 98.13% producer accuracy and 93.34% user accuracy of tea plantations. The results indicated OBIA could overcome the shortcomings of the PBIA and effectively improve the mapping accuracy of tea plantation, and OBIA integrating GF-5, HH, and VV polarized data could play a distinguished role in tea plantation mapping. This work provides a promising approach for mapping tea plantations and demonstrates that the integration of GF-5 and copolarized data can improve spectral separability of vegetation, which is also significant for general forest mappings.

Uncertainty and Overfitting in Fluvial Landform Classification Using Laser Scanned Data and Machine Learning: A Comparison of Pixel and Object-Based Approaches

Floodplains are valuable scenes of water management and nature conservation. A better understanding of their geomorphological characteristic helps to understand the main processes involved. We performed a classification of floodplain forms in a naturally developed area in Hungary using a Digital Terrain Model (DTM) of aerial laser scanning. We derived 60 geomorphometric variables from the DTM and prepared a geomorphological map of 265 forms (crevasse channels, point bars, swales, levees). Random Forest classification was conducted with Recursive Feature Elimination (RFE) on the objects (mean pixel values by forms) and on the pixels of the variables. We also evaluated the classification probabilities (CP), the spatial uncertainties (SU), and the overfitting in the function of the number of the variables. We found that the object-based method had a better performance (95%) than the pixel-based method (78%). RFE helped to identify the most important 13–20 variables, maintaining the high model performance and reducing the overfitting. However, CP and SU were not efficient measures of classification accuracy as they were not in accordance with the class level accuracy metric. Our results help to understand classification results and the specific limits of laser scanned DTMs. This methodology can be useful in geomorphologic mapping.

Analysis of Land Use Land Cover Dynamics and Driving Factors in Desa’a Forest in Northern Ethiopia

Understanding the dynamic of land use/land cover (LULC) can assist relevant authorities in implementing them along with management options for natural forests like Desa’a forest in Northern Ethiopia. It is worth mentioning that no comprehensive study has been done so far on the overall change of LULC within the surrounding of Desa’a forest areas. This study aimed at analyzing the LULC changes and driving factors in Desa’a forest through Landsat images (i.e., 1973 (MSS), 1986 (TM), and 2015 (OLI)) and maximum likelihood supervised classification. Post classification pixel to pixel comparison was also performed to detect changes from year to year. Finally, focus group discussions were made to identify the LULC change drivers and support the computer-based classifications accuracy. Results demonstrated that both dense (54.11%) and open forest (28.48%) in 1973 covered almost 83% of the total forest area while only 17% was covered by grasslands (10%), farmland (3%), and bare land (4%). This is despite the fact that woody vegetation covered only 39% of the total forest reserve in 2015. During the study period, almost 54,000 ha of grasslands and open and dense forests have been converted to either bare land or farmland, of which 48,163 ha were previously open dense forests. Continuing with such a trend will lead to more severe deforestation and it is necessary to identify and modify the driving variables in Desa’a forest. The results also showed that fire, agricultural expansion, grazing and browsing effect, drought, extraction of wood, and lack of government attention are the major deforestation driver in Desa’a forest. The results of this study can provide a practical perspective for land use planners to manage LULC changes, reduce greenhouse gases, and enhance the biodiversity conservation in the region. In addition, the findings of the current study could provide a perfect source for calculations and choosing the best policy-making decisions to protect and manage plans for Desa’a forest.

BUILDING EXTRACTION FROM VERY HIGH-RESOLUTION SATELLITE IMAGES FOR MAP UPDATING IN EGYPT

Robust building detection from satellite images has been a subject of interest for several decades. Very High Resolution (VHR) satellite images support the efficient extraction of manmade objects. The main aim of this paper is to present an approach for building extraction from VHR satellite images for map updating in Egypt. To achieve this aim, a comparison of pixel and object-based classification techniques has been applied. Then, different refinement processes based on shadow, context, shape, and Digital Surface Model (DSM) data are carried out. Two study areas from the VHR satellite images for Assuit and Sohag cities are used. A comparison of the classification techniques shows that the Maximum Likelihood Classifier (MLC) for pixel-based technique and Support Vector Machine (SVM) for object-based technique give the highest overall accuracy results. Refinement based on shadow, context, shape, and DSM information improves the overall accuracy with an average of 18%. Thus, the building extraction results can contribute significantly to update maps in Egypt.

Specimen Authentication System Using Pixel-Based Algorithm

Specimen Authentication is the process used to recognize an individual's handwritten signature. The pre-process of verifying a signature is Pixel-based. There is a need for an automatic verification system for a signature since the signature has been a means of a person's identification for ages. Offline systems work on the scanned image of a signature. The main objective of the study was to design and develop a Specimen Authentication System to examine signatures. In this study, the researchers verify the off-line signatures by taking a boundary of the entire signature and make the pixel comparison. Signature is acquired using a scanner. The detection process is done after the data acquisition and pre-processing. Pre-processing includes noise removal, grey-scale, manipulation, edge detection. Experimental results show that 50% of the accurate matching with the existing one. There is an important distinction between simple signature Comparisons and dynamic signature verification. Both can be computerized, but a Simple comparison only takes into account what the signature looks like. Dynamic signature verification takes into account how the signature was made. With dynamic signature verification, it is not the shape or looks of the meaningful signature; it is the change in speed, pressure, and timing that occur during the act of signing. Consistency created by natural motion and practice over time creates a recognizable pattern that makes the hand-written signature a natural for biometric identification.

Assessing the Performance of Methods for Monitoring Ice Phenology of the World’s Largest High Arctic Lake Using High-Density Time Series Analysis of Sentinel-1 Data

Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015–2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3–10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2–10 days. Mean errors for comparisons of different sections of the lake ranged 0–15 days for the first difference method and 2–17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring.

Cascaded Stripe Memory Engines for Multi-Scale Object Detection in FPGA

Object detection in embedded systems is important for many contemporary applications that involve vision and scene analysis. In this paper, we propose a novel architecture for object detection implemented in FPGA, based on the Stripe Memory Engine (SME), and point out shortcomings of existing architectures. SME processes a stream of image data so that it stores a narrow stripe of the input image and its scaled versions and uses a detector unit which is efficiently pipelined across multiple image positions within the SME. We show how to process images with up to 4K resolution at high frame rates using cascades of SMEs. As a detector algorithm, the SMEs use boosted soft cascade with simple image features that require only pixel comparisons and look-up tables; therefore, they are well suitable for hardware implemenation. We describe the components of our architecture and compare it to several published works in several configurations. As an example, we implemented face detection and license plate detection applications that work with HD images (1280 ×720 pixels) running at over 60 frames/s on Xilinx Zynq platform. We analyzed their power consumption, evaluated the accuracy of our detectors, and compared them to Haar Cascades from OpenCV that are often used by other authors. We show that our detectors offer better accuracy as well as performance at lower power consumption.

Comparison of pixel to pixel and object-based image analysis with using WorldView-2 satellite images of Yangiobod village of Syrdarya province

Currently, more than 300 satellites have been launched into space and providing us with information about the Earth and processes which happens in there. Those information is very useful in all branches. These satellites started to modify and modernize year by year. Especially after 2000, satellites of very high resolution were launched into space. These satellites are sending information with very high resolution. To improve the speed and accuracy of the analysis of these images, scientists have developed a number of methods and programs. As a result, users often find face to difficulties with knowing which method or program is most effective. In this article, analyzed many researches and scientific studies and analyzed WorldView-2 (WV2) images of the Syrdarya Province based on field experiments and outlined the advantages and disadvantages of the method and tool. WV2 images are very important and provide much relevant data for all image analysis. VHR of these images can increase the quality and possibilities of all analysis. But usage of these images globally has not developed because of their costs. Square of satellite image capturing is very little for global analysis. to do global analysis we need 100 s of this image. That is why scientists use this data more often for correlation or creating general methods. That is why it has not been used for regional and global analysis. In our research, we used GEOBIA’s eCognition software. The accuracy of this program is 95 %. In arid regions like Uzbekistan, we recommend optimal software, analyse steps and data.

Color Correction Based on CFA and Enhancement Based on Retinex With Dense Pixels for Underwater Images

Color correction and enhancement for underwater images is challenging due to attenuation and scattering. The underwater images often have low visibility and suffer from color bias. This paper presents a novel color correction method based on color filter array (CFA) and an enhancement method based on Retinex with dense pixels and adaptive linear histogram transformation for degraded color-biased underwater images. For any digital image in the RGB space, which is captured by digital camera with CFA, their RGB values are dependent and coupled because of the interpolation process. So we try to compensate red channel attenuation of underwater degraded images from the green channel and blue channel. Retinex model has been widely used to efficiently handle low brightness and blurred images. The McCann Retinex (MR) method selects a spiral path for pixel comparison to estimate illumination. However, the simple path selection doesn't include global light dark relationship of the whole image. So we design a scheme to gain much well-distributed and denser pixels to obtain more precise intensity of illumination. Besides, we design a piecewise linear function for histogram transform, which is adaptive to the whole RGB value. Experiments on a large number of underwater degraded images show that, the processed images by our method have clearer details and uniform visual effect for all channels in RGB color space and our method can also obtain good performance metrics.

Comparison of satellite remote sensing derived precipitation estimates and observed data in Kenya

This study evaluated the accuracy of four satellite remote sensing (SRS) based products in predicting rainfall (amounts and spatial distribution) over Kenya between 1998 and 2013. The four SRS products used include; two satellite products (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS 2.0) and Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 (TRMM)), one gauge-interpolated product (Global Precipitation Climatology Centre (GPCC)) and one re-analysis product (Modern-Era Retrospective Analysis for Research and Application (MERRA)). The monthly precipitation data were evaluated for completeness, converted to individual raster files, projected to the World Geodetic System (WGS) 1984 - Universal Transverse Mercator (UTM) zone 37 N ensuring a similar processing extent, rescaled to a common resolution and reclassified following defined uniform intervals for ease of comparison. Thereafter, they were subjected to five different metrics based on eight agro-ecological zones (AEZs) of Kenya, in reference to observed rainfall data obtained from Kenya meteorological department (KMD). Results show that all SRS products both overestimated or underestimated rainfall amounts on a pixel to pixel comparison. Based on point to point proportion of variance evaluation (r2), TRMM best-estimated rainfall in the tropical cool humid (r2 = 0.64), tropical warm humid (r2 = 0.58) and tropical cool subhumid (r2 = 0.39) zones and can be used for agricultural advisory services. The GPCC product best-estimated rainfall in the tropical warm semiarid (r2 = 0.46) and warm tropical sub-humid (r2 = 0.21), while CHIRPS 2.0 best-estimated rainfall in the tropical warm arid (r2 = 0.33) and therefore the two products could be best used to predict rainfall in the ASALs and drought-related studies, with potential for irrigation. The MERRA product best-estimated rainfall in tropical cool arid (r2 = 0.97) and tropical cool semiarid (r2 = 0.53) and could, therefore, be best used for high elevation and drought-related studies. These results demonstrate the promising potential of the satellite remote sensed data in complementing the existing meteorological observed data which are often marred by inconsistency and scarcity, and hence unreliable in the existing agricultural advisory and other climate-based applications in Kenya, and sub-Saharan Africa at large. However, given the observed AEZ dependant variations in the satellite estimates, it is advisable to choose the most suitable SRS product for specific activities per AEZ and calibrate before utilisation.

Lemp: a Robust Image Feature Descriptor for Retrieval Applications

line edge magnitude pattern (lemp) is proposed in this paper. Line edge distribution is used to denote local region of an image. Popular texture descriptors such as lbp deal with a comparison of centre pixel with neighbors and thus encode the information. In lemp ,pixel at the centre is replaced by edge values of neighbors. Discriminating information provided by line edges makes this method different from many of the existing methods. Magnitude is also added to the line edge information in order to make the feature descriptor more effective and robust. Performance of lemp method is estimated with corel database. Standard metrics such as recall, precision and average retrieval rate are determined for comparison purpose. Experimental values exhibit a notable improvement in the performance.

INTER-COMPARISON OF THE GLOBAL LAND COVER MAPS IN AFRICA SUPPLEMENTED BY SPATIAL ASSOCIATION OF ERRORS

Abstract. Recent advances in Earth Observations supported development of high-resolution land cover (LC) maps on a large-scale. This is an important step forward, especially for developing countries, which experienced problems in the past due to absence of reliable LC information. Nevertheless, increasing number of LC products is imposing additional validation workload to confirm their quality. In this paper inter-comparison of two recent LC products (GlobeLand30 and S2 prototype LC 20m map of Africa) for country of Rwanda in Africa was done. It is a way to facilitate validation by identifying the areas with higher probability of error. Specific approach of comparison of single pixel of one map with multiple pixels of another map provided confusion matrix and sub-pixel agreement table. In this work, accuracy indexes based on the confusion matrix were computed as a measure of similarity between the two maps. Furthermore, Moran’s I index was computed for estimation of spatial association of the pixels in disagreement. Also, total disagreement, as well as disagreement of particularly confused classes was visualised to analyse their spatial distribution. The results are showing that similarity of the two maps is about 66%. Disagreements are spatially associated and the most evident in the eastern and north-western part of the area of interest. This coincides also with the distribution of the two most confused classes Wetland and Shrubland. The results delineate areas of inconsistency between the two maps, and therefore areas where careful accuracy analysis are needed.

Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study

Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision.

Intelligent Facial Emotion Recognition

The system introduces an intelligent facial emotion recognition using arti?cial neural network (ANN). The concept ?rst takes modi?ed local binary patterns, which involve horizontal vertical and neighborhood pixel comparison, to produce initial facial representation. Then, a microgenetic algorithm(mGA) embedded Particle Swarm Optimization(PSO), is proposed for feature optimization. It incorporates a nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a sub dimension based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Arti?cial Neural Network is used as a classi?er for recognizing seven facial emotions. ANN is implemented as classi?er for pattern recognition. Based on a comprehensive study using within- and cross-domain images from the extended Japanese database. The empirical results indicate that our proposed system outperforms other state of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.

METHOD OF PROTECTION OF MATERIAL INFORMATION CARRIED OUT BY LATENT ELEMENTS

The method of protection of latent elements for the printed documents at the stage of preprint preparation with the further withdrawal of printing forms and prints is developed. The choice of the minimum thickness of lines for the offset printing method is substantiated. The measurements of the relative size of the raster point, optical light shrinkage when printed on different papers are studied using the Sheberstov-Murray-Davis formula. The study of the properties of fifteen paper models used for printing and their densitometric indices, which influence the quality of printing, are shown. The comparison of thin lines with offset printing and office equipment, which uses the method of printing an electrophotography, is carried out. Digital protective latent elements are developed. A graphical way of protecting printed documents is created by creating hidden images when choosing high resolution lines. The method of protection is that for the protective elements, the displacement of the part of the lines of the raster is formed by half the size of the step of the raster lines. Improved latent element protection by overlaying fractal nets, which makes it possible to create graphic traps. The printing of developed protective elements on paper models has been investigated. Protective items are printed on Heidelberg offset printing machine. From the prints there are made copies which, compared with the originals, using the method of pixel comparisons of the signal-to-noise ratio, the results of comparing the originals among themselves, the originals and their copies are presented in this paper. Experimental research was conducted on the basis of the Full Pixel Comparison (PSNR) method of the original of the protective element printed on the offset machine with the original, and the original with its copy. On the basis of researches the reliability of documents is established. Also compare the originals of documents among themselves.

The Comparison of Pixel and Object-based Classification for Land Use/Land Cover Mapping

Yüksek çözünürlüklü uydu görüntülerinin sınıflandırılmasıyla oluşturulan arazi örtüsü-arazi kullanımı (AÖ/AK) envanterleri, hem kent ölçeğinde hem de peyzaj ölçeğindeki planlama uygulamalarının yürütülmesi açısından önem taşımaktadır. Avrupa'da alan kullanım planlaması kapsamında hazırlanan kent atlasları, peyzaj ölçeğindeki Natura 2000 ve CORINE veri setlerine dayanan haritalar, uydu görüntülerinin yüksek doğrulukla sınıflandırılması ile oluşturulmaktadır. Sınıflandırma tekniklerinden kontrollü ve kontrolsüz sınıflandırmanın yanı sıra, güncel bir yöntem olan obje tabanlı sınıflama da etkin olarak kullanılmaktadır. Bu çalışmanın materyali olarak, 2013 yılına ait Aydın kentsel yerleşim merkezi sınırındaki 0.5 m yersel çözünürlüğe sahip Worldview Ortoready Pansharpened uydu görüntüsü ve görüntünün yorumlanmasında Google Earth Pro yazılımının geçmiş görüntüleri arasında aynı yılı kapsayan hava fotoğrafları kullanılmıştır. Kentsel alan kullanımlarını örnekleyecek düzeyde seçilen çalışma alanı, kontrollü ve obje tabanlı sınıflandırma tekniği ile sınıflandırılarak, sınıflandırma sonuçları karşılaştırmalı olarak değerlendirilmiştir. Doğruluk analizi sonuçlarına göre, kontrollü sınıflandırma için hesaplanan ortalama doğruluk değeri %82.79, obje tabanlı sınıflandırma için hesaplanan ortalama doğruluk değeri %92.52 olarak elde edilmiştir.

A random forest classifier based on pixel comparison features for urban LiDAR data

The outstanding accuracy and spatial resolution of airborne light detection and ranging (LiDAR) systems allow for very detailed urban monitoring. Classification is a crucial step in LiDAR data processing, as many applications, e.g., 3D city modeling, building extraction, and digital elevation model (DEM) generation, rely on classified results. In this study, we present a novel LiDAR classification approach that uses simple pixel comparison features instead of the manually designed features used in many previous studies. The proposed features are generated by the computed height difference between two randomly selected neighboring pixels. In this way, the feature design does not require prior knowledge or human effort. More importantly, the features encode contextual information and are extremely quick to compute. We apply a random forest classifier to these features and a majority analysis postprocessing step to refine the classification results. The experiments undertaken in this study achieved an overall accuracy of 87.2%, which can be considered good given that only height information from the LiDAR data was used. The results were better than those obtained by replacing the proposed features with five widely accepted man-made features. We conducted algorithm parameter setting tests and an importance analysis to explore how the algorithm works. We found that the pixel pairs directing along the object structure and with a distance of the approximate object size can generate more discriminative pixel comparison features. Comparison with other benchmark results shows that this algorithm can approach the performance of state-of-the-art deep learning algorithms and exceed them in computational efficiency. We conclude that the proposed algorithm has high potential for urban LiDAR classification.

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

This study presents a novel noise detection algorithm which satisfactorily detects noisy pixels in images corrupted by random-valued impulse noise of high levels up to 80% noise density. Three levels of adaptive thresholds along with an auxiliary condition are used in this method which adequately addresses the drawbacks of existing methods, especially the miss detection of noise-free pixels as noisy pixels and vice versa. A noise signature is calculated for every pixel and compared with the first threshold to identify noise followed by the comparison of the central pixel with the second and third levels of thresholds. In addition to the standard deviation and mean, the concept of quartile has been used as another measure of dispersion. After detection, a fuzzy switching weighted median filter is applied to restore the corrupted image. The simulation results demonstrate that the proposed method is able to outperform the existing methods in both the detection and filtering of random-valued impulse noise in images.