Texture Measures Research Articles

Predicting measures of soil health using the microbiome and supervised machine learning

Soil health encompasses a range of biological, chemical, and physical soil properties that sustain the commercial and ecological value of agroecosystems. Monitoring soil health requires a comprehensive set of diagnostics that can be cost-prohibitive for routine analyses. The soil microbiome provides a rich source of information about soil properties, which can be assayed in a high-throughput, cost-effective way. We evaluated the accuracy of random forest (RF) and support vector machine (SVM) regression and classification models in predicting 12 measures of soil health, tillage status, and soil texture from 16S rRNA gene amplicon data with an operationally relevant sample set. We validated the efficacy of the best performing models against independent datasets and also tested best practices for processing microbiome data for use in machine learning. Soil health metrics could be predicted from microbiome data with the best models achieving a Kappa value of ∼0.65, for categorical assessments, and a R2 value of ∼0.8, for numerical scores. Biological health ratings were better predicted than chemical or physical ratings. Validation with independent datasets revealed that models had general predictive value for soil properties, including yield. The ecological profiles of several taxa important for model accuracy matched the observed relationships with soil health, including Pyrinomonadaceae, Nitrososphaeraceae, and Candidatus Udeaobacter. Models trained at the highest taxonomic resolution proved most accurate, with losses in accuracy resulting from rarefying, sparsity filtering, and aggregating at higher taxonomic ranks. Our study provides the groundwork for developing scalable technology to use microbiome-based diagnostics for the assessment of soil health.

Power Spectrum Analysis of Breast Parenchyma with Digital Breast Tomosynthesis Images in a Longitudinal Screening Cohort from Two Vendors

To quantitatively compare breast parenchymal texture between two Digital Breast Tomosynthesis (DBT) vendors using images from the same patients. This retrospective study included consecutive patients who had normal screening DBT exams performed in January 2018 from GE and normal screening DBT exams in adjacent years from Hologic. Power spectrum analysis was performed within the breast tissue region. The slope of a linear function between log-frequency and log-power, β, was derived as a quantitative measure of breast texture and compared within and across vendors along with secondary parameters (laterality, view, year, image format, and breast density) with correlation tests and t-tests. A total of 24,339 DBT slices or synthetic 2D images from 85 exams in 25 women were analyzed. Strong power-law behavior was verified from all images. Values of β d did not differ significantly for laterality, view, or year. Significant differences of β were observed across vendors for DBT images (Hologic: 3.4±0.2 vs GE: 3.1±0.2, 95% CI on difference: 0.27 to 0.30) and synthetic 2D images (Hologic: 2.7±0.3 vs GE: 3.0±0.2, 95% CI on difference: -0.36 to -0.27), and density groups with each vendor: scattered (GE: 3.0±0.3, Hologic: 3.3±0.3) vs. heterogeneous (GE: 3.2±0.2, Hologic: 3.4±0.1), 95% CI (-0.27, -0.08) and (-0.21, -0.05), respectively. There are quantitative differences in the presentation of breast imaging texture between DBT vendors and across breast density categories. Our findings have relevance and importance for development and optimization of AI algorithms related to breast density assessment and cancer detection.

Geodiversity underpins biodiversity but the relations can be complex: Implications from two biodiversity proxies

Geodiversity incorporates many of the environmental patterns and processes that are considered determinants of biodiversity. However, research on the spatial relations between geodiversity and biodiversity at large regional scales is challenging because estimating biodiversity from field data is usually time consuming and costly. Satellite image textural measures and landscape metrics have been reported as powerful tools and proxies to characterize regional biodiversity. The goal of this study is to explore the spatiotemporal variations in two different biodiversity proxies and their spatial relations with geodiversity in Loess Plateau, China. Geographically-weighted spatial correlation was used to characterize the non-stationary spatial relations between geodiversity and the two proxy measures. The results showed that textural measures and landscape metrics exhibit different features of the spatiotemporal variations, highlighting their complementarity and consequent relevance in conjoint application. Trend analysis of the textural measures during 2000–2015 indicated that habitat heterogeneity increased in most of the region (39.87–62.41%), particularly in the central and southwestern areas. Nonetheless, the landscape metrics decreased in the central and southwestern region (38.14–71.94%), particularly in the Ziwuling and Huanglong forest area and part of Gansu province. The magnitude and direction of the relations between geodiversity and the two biodiversity proxies were context-dependent and varied spatially. Despite the contrasting direction in the relations between geodiversity and textural measures/landscape metrics in the southeastern region, stronger relations indicated by greater absolute value of the correlation coefficient were found in human-dominated areas than those in other areas. Informed by these results, we recommend focusing primarily on the human-dominated landscapes and the areas with high geodiversity to promote conservation planning with geodiversity and biodiversity as equal, interrelated concepts.

Image Processing Based Various Strategies of Lung Cancer Diagnosis

Medical imaging represents the utilization of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient’s life through a more precise and rapid diagnosis and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this work is to study the tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CT) and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab) normality of tissue and this work expands this idea to tumour texture grading and classification. The fractal dimension (FD) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CT modality, and quantitative performance analysis showed an accuracy of in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour DM images via a set of statistical and model-based texture analysis algorithms.

Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield

The speedy development of UAV (Unmanned Aerial Vehicle) has provided more data choices for crop yield estimation. In most cases, spectral information derived from multispectral or hyperspectral images is used alone for yield estimation, while variables that contain spatial information such as textural measures were not considered. As UAV can acquire images with spatial resolution at centimeter-level, which contain rich spatial information of observed objects, the test of textural measures in improving yield estimation becomes possible. In this study, UAV-based hyperspectral images of rice in the east of China were acquired in 2017, 2018 and 2019 three consecutive year during rice growing seasons. Eight gray level co-occurrence matrix (GLCM) based texture measures were derived, and all two band combinations of these textural measures as well as spectral reflectance in three types (normalized type, differential type and simple ratio type) were calculated to developing textural and spectral index. Grain yield estimation models were established using spectral indices alone and spectral indices combining textural measures, respectively. The results show that models based on spectral indices combining textural measures all performed better than these using spectral indices alone, with coefficient of determination R2 of the best model greater than 0.8, RMSE (Root Mean Square Error) of 0.421 Mg ha−1 and MAPE (Mean Absolute Percentage Error) of 4.66% for calibration results, and 0.521 Mg ha−1 and 6.63% for validation results. Meanwhile, spectral indices combining textural measures can adjust the problems of overestimation and underestimation in models developed with spectral indices alone. This kind of improvement is especially obvious when grain yield is below 5.0 Mg ha−1 or above 8.0 Mg ha−1. Besides, the estimation accuracy of the model based on spectral index combining textural measure at the booting stage was equivalent to the model based on spectral indices at the booting and heading stages, indicating that textural measures may be helpful in monitoring grain yield at an earlier stage.

Towards a workflow for operational mapping of Aedes aegypti at urban scale based on remote sensing

Remote sensing (RS) applications for vector borne diseases are a field of high social impact increasingly relevant in the context of a higher frequency of Dengue, Chikungunya and Zika outbreaks at global scale and especially in Latin America. The operative use of RS technologies is however still rare. Therefore, the objective of this work is to generate and analyze multitemporal Aedes aegypti's suitability maps and to share the open source tools used towards the building of an operative workflow. As a proof of concept, we implemented a process chain to obtain maps for Ae. aegypti activity within the 2017–2018 mosquito breeding season based on ovitraps records and RS data within the framework of ecological niche modeling. The workflow was carefully thought as to consider possible biases in training data, model calibration to attain the best hyper-parameter combination, model selection, variable selection and validation with independent data. The predictive maps showed high suitability for Ae. aegypti within the city, except in large vegetated areas and the commercial downtown consistently with previous studies and our own observations. Relevant variables included distance to built-up surfaces, distance to vegetated areas and correlation, a texture measure reflecting surface heterogeneity. Validation results suggested that the spatial distribution of ovitraps should be re-examined. All the steps in the proposed workflow were implemented using freely available and open source software, which warrants reproducibility and allows for re-use and modifications in terms of methods and RS or mosquito data available.

Lognormal Random Field models to identify temporal Land cover changes using full polarimetric L-Band SAR imagery

Multiplicative Autoregressive Random Field (MAR) based texture models have been identified as one of the most appropriate models for SAR intensity images to capture the stochastic spatial interaction among neighboring pixels. But very few studies have tested their viability particularly in disaster applications. In this paper, we analyse the MAR texture models for their advantageous in land cover change detection compared to the changes resulting from logarithm of SAR image intensity and speckle filtered SAR imagery. The paper shows that lognormal random fields with multiplicative spatial interactions in the form of MAR models can be an effective alternative to suppress speckle noise and model SAR image intensity in time series data analysis. The pre and post disaster observational data of the Tohoku earthquake, in the east coast of Japan, acquired by the Advanced Land Observation Satellite (ALOS)/phased array type L-band synthetic aperture radar (PALSAR) were synthesized using MAR model based texture measures. Two of the main texture descriptors of the MAR model were considered primarily in this study. Those are the neighborhood weighting and the noise variance parameters. A 2nd order neighborhood configuration was used to estimate them. We present a variogram based analysis, structural similarity index measure (SSIM), and the mean ratio detector (MRD) as three different approaches to analyse the changes in land cover using radar texture. The change detection results of the MRD were further tested using area error proportion (AEP), root mean square error (RMSE) and correlation coefficient (CC), keeping normalized ratio, principle component analysis (PCA) and adaptive Lee filtered polarimetric intensity based change as the references.

Optimising Sentinel-2 MSI spatial resolution for estimating foliar nitrogen concentration in miombo woodlands

ABSTRACT The landscape-level estimation of nitrogen will constitute a significant input in global change models. High resolution broadband satellite technology is a promising solution to the limitation of spatial coverage. This study sought to identify an optimal spatial resolution for estimating foliar nitrogen in dry miombo woodlands. The contrast texture measure, calculated at increasing window size on Sentinel-2 imagery, was related to field measured nitrogen via random forest regression models. Our results show that the optimum size for estimating nitrogen is 50m. This result is significant in understanding the scale at which dynamics in foliar nitrogen concentration can be remotely measured.

Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions

Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture measures such as entropy and contrast, capturing the neighboring variation of MRI voxel intensities, were computed and fed into deep learning technique for group classification. Layer-wise relevance was applied for the localization of the classification results. Texture feature map of non-segmented brain MRI scans were extracted from 141 SCZ, 103 MD and 238 HC. The gray level co-occurrence matrix (GLCM) was calculated on a voxel-by-voxel basis in a cube of voxels. Deep learning tested if texture feature map could predict diagnostic group membership of three classes under a binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method was applied in a repeated nested cross-validation scheme and cross-validated feature selection. The regions with the highest relevance (positive/negative) are presented. The method was applied on non-segmented images reducing the computation complexity and the error associated with segmentation process.

Investigation of Informal Settlement Indicators in a Densely Populated Area Using Very High Spatial Resolution Satellite Imagery

Automation of informal settlements detection using satellite imagery remains a challenging task in urban remote sensing. This is due to the fact that informal settlements vary in shape, size and spatial arrangement from one region to the other in some cases within a city. This paper investigated the methodology to detect informal settlements in a densely populated township by assessing informal settlement indicators observed from very high spatial resolution satellite imagery. We assessed twelve informal settlement indicators to determine the most effective indicators to distinguish between informal and informal classes. These indicators included the spectral indices first and second-order statistical measurements. In addition to the commonly used informal settlement indicators, we assessed the effectiveness of built-up area and iron cover. The GLCM textural measures performed poorly in separating informal and formal settlements compared to first-order statistics measurement and spectral indices. The built-up area index, coastal blue index and the first-order statistics mean measurements produced higher separability distance of informal and formal settlements. The iron index performed better in separating the two settlement types than the commonly used GLCM measure and NDVI. The proposed ruleset that uses the three features with the highest separability distance achieved producer and user accuracies of informal settlements of 95% and 82%, respectively. The results of this study will contribute towards developing methodologies to automatically detect informal settlements.

HISTOPATHOLOGY IMAGE REGISTRATION BY INTEGRATED TEXTURE AND SPATIAL PROXIMITY BASED LANDMARK SELECTION AND MODIFICATION.

Three-dimensional (3D) digital pathology has been emerging for next-generation tissue based cancer research. To enable such histopathology image volume analysis, serial histopathology slides need to be well aligned. In this paper, we propose a histopathology image registration fine tuning method with integrated landmark evaluations by texture and spatial proximity measures. Representative anatomical structures and image corner features are first detected as landmark candidates. Next, we identify strong and modify weak matched landmarks by leveraging image texture features and landmark spatial proximity measures. Both qualitative and quantitative results of extensive experiments demonstrate that our proposed method is robust and can further enhance registration accuracy of our previously registered image set by 31.15% (correlation), 4.88% (mutual information), and 41.02% (mean squared error), respectively. The promising experimental results suggest that our method can be used as a fine tuning module to further boost registration accuracy, a premise of histology spatial and morphology analysis in an information-lossless 3D tissue space for cancer research.

Effects of composite geometric characteristics of coarse particles on interface interactions of aggregate-asphalt system

The complex geometric morphologies and contact status of aggregate particles and the interface interactions between aggregate and asphalt significantly affect the compaction properties and mechanical response of asphalt mixtures. The spreading-combination method was proposed to build the calculation model of composite geometric indexes to quantitatively characterize the composite geometric characteristics of the particle system. The effects of composite geometric characteristics on the interface interactions of the aggregate-asphalt system were analyzed adopting the contact-slip tester. The results indicate that the measures of shape, texture, and angularity respectively denote perimeter, surface area, and volume. The composite shape index is primarily affected by the number of particles. Smaller-sized coarse aggregates and fine aggregates exercise a greater impact on the composite texture index, while the composite angularity index is more sensitive to large-sized coarse aggregates. The fine particles attach to the surface of coarse aggregates, diminishing the contributions of the composite texture characteristics of coarse aggregates to the particle contact strength of the particle system. The larger the composite angularity index, the more significant the lubricating effect of asphalt on particle contact. However, asphalt can transform a part of the interference effect of the fine aggregates into the bonding effect of the skeleton. The composite angularity index is positively correlated with the skeleton bonding index, which can be used to evaluate and predict the interface interactions of the aggregate-asphalt system.

Meat Tenderness: Underlying Mechanisms, Instrumental Measurement, and Sensory Assessment

Meat tenderness is a quality trait critical to consumer acceptance and determines satisfaction, repeat purchase, and willingness to pay premium prices. The aim of this review was to explore instrumental and sensory methods for assessing meat tenderness in relation to underlying mechanisms and to identify limitations of existing methods, as well as opportunities for global standardization. To achieve this, tenderness is defined, and the main instrumental methods for tenderness are presented, including their historical development and standardization. The significant determinants of meat tenderness are presented, encompassing connective tissue and cross-links, myofibrillar integrity, sarcomere length, intramuscular fat, and protein denaturation during cooking. The development of sensory methods for assessing meat is presented as well as the link between objective measures of texture and consumer tenderness scores. Recent advances in statistical methods for sensory data are discussed, and considerations for future research are outlined.

Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019

A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm2/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (<0.6 nW/cm2/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses.

MR method for measuring microscopic histologic soft tissue textures.

Provide a direct, non-invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases. MR micro-Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k-values to characterize texture-as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k-value phase encode to the excited spins in the VOI, and acquiring each individual k-value data point in a single excitation-providing motion immunity and extended acquisition time for maximizing signal-to-noise ratio. Additional k-value measurements from the same tissue can be made to characterize the tissue texture in the VOI-there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue. Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented. The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging.

Texture feature column scheme for single‐ and multi‐script writer identification

Identification of writers from images of handwriting is an interesting research problem in the handwriting recognition community. Application of image analysis and machine learning techniques to this problem allows development of computerised solutions which can facilitate forensic experts in reducing the search space against a questioned document. This article investigates the effectiveness of textural measures in characterising the writer of a handwritten document. A novel descriptor by crossing the local binary patterns (LBP) with different configurations that allows capturing the local textural information in handwriting using a column histogram is introduced. The representation is enriched with the oriented Basic Image Features (oBIFs) column histogram. Support vector machine (SVM) is employed as the classifier, and the experimental study is carried out on five different datasets in single as well as multi-script evaluation scenarios. Multi-script evaluations allow evaluating the hypothesis that writers share common characteristics across multiple scripts and the reported results validate the effectiveness of textural measures in capturing this script-independent, writer-specific information.

A Smaller Bearing Ratio, as a Surface Texture Measure, Promotes Faster Sliding on Ice

Abstract Surfaces only characterized by a roughness Ra or Sa may have a totally different surface texture and include complex patterns such as grooves, dimples, or a mirror-polish. Here, the bearing ratio is proposed as an additional characterization measure to determine the sliding performance of a steel–ice friction pair. Different steel surfaces were produced by milling, shot blasting, and scratching, followed by texture assessment with a stylus type three-dimensional (3D) profilometer. The bearing ratio and other 3D roughness parameters were determined. Tribology experiments involved a 3 m long inclined plane tribometer and the speed measured at four points during the sliding experiment. Correlation between the steel sliding speed and the bearing ratio was observed under two different regimes: at warmer conditions and at colder conditions. Experiment 1 depicting warmer conditions exhibited a relative humidity of 64%, an air temperature of −2 °C, and an ice temperature of −9 °C. Experiment 2 for colder conditions showed a relative humidity of 78%, an air temperature of 1 °C, and an ice temperature of −4 °C. The sliding speed correlated with the bearing ratio in these two conditions showing −0.91 and −0.96, respectively. A strong correlation between the sliding speed and the bearing ratio shows the value of the bearing ratio as an additional surface characteristic for considering larger surface features.

Assessment of Landsat Based Deep-Learning Membership Analysis for Development of from–to Change Time Series in the Prairie Region of Canada from 1984 to 2018

The prairie region of Canada is a dynamically changing landscape in relation to past and present anthropogenic activities and recent climate change. Improving our understanding of the rate, timing, and distribution of landscape change is needed to determine the impact on wildlife populations and biodiversity, ultimately leading to better-informed management regarding requirements for habitat amount and its connectedness. In this research, we assessed the viability of an approach to detect from–to class changes designed to be scalable to the prairie region with the capacity for local refinement. It employed a deep-learning convolutional neural network to model general land covers and examined class memberships to identify land-cover conversions. For this implementation, eight land-cover categories were derived from the Agriculture and Agri-Food Canada Annual Space-Based Crop Inventory. Change was assessed in three study areas that contained different mixes of grassland, pasture, and forest cover. Results showed that the deep-learning method produced the highest accuracy across all classes relative to an implementation of random forest that included some first-order texture measures. Overall accuracy was 4% greater with the deep-learning classifier and class accuracies were more balanced. Evaluation of change accuracy suggested good performance for many conversions such as grassland to crop, forest to crop, water to dryland covers, and most bare/developed-related changes. Changes involving pasture with grassland or cropland were more difficult to detect due to spectral confusion among classes. Similarly, conversion to forests in some cases was poorly detected due to gradual and subtle change characteristics combined with confusion between forest, shrub, and croplands. The proposed framework involved several processing steps that can be explored to enhance the thematic content and accuracy for large regional implementation. Evaluation for understanding connectivity in natural land covers and related declines in species at risk is planned for future research.

Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data

The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algorithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project.

Urban Feature Extraction from Merged Airborne LiDAR Data and Digital Camera Data

Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images. In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used. Firstly, image orientation, AT have been performed. Then the automatic digital surface model DSM generation has been produced from the digital aerial camera. Thirdly true digital ortho has been generated from the digital aerial camera also orthoimage will be generated using LIDAR digital elevation model (DSM). Leica Photogrammetric Suite (LPS) module of Erdsa Imagine 2014 software was utilized for processing. Then the resulted orthoimages from both techniques were mosaicked. The results show that automatic digital surface model DSM that been produced from digital aerial camera method has very high dense photogrammetric 3D point clouds compared to the LIDAR 3D point clouds. It was found that the true orthoimage produced from the second approach is better than the true orthoimage produced from the first approach. The five approaches were tested for classification of the best-orthorectified image mosaic using subpixel based (neural network) and pixel-based ( minimum distance and maximum likelihood).Multicues were extracted such as texture(entropy-mean),Digital elevation model, Digital surface model ,normalized digital surface model (nDSM) and intensity image. The contributions of the individual cues used in the classification have been evaluated. It was found that the best cue integration is intensity (pan) +nDSM+ entropy followed by intensity (pan) +nDSM+mean then intensity image +mean+ entropy after that DSM )image and two texture measures (mean and entropy) followed by the colour image. The integration with height data increases the accuracy. Also, it was found that the integration with entropy texture increases the accuracy. Resulted in fifteen cases of classification it was found that maximum likelihood classifier is the best followed by minimum distance then neural network classifier. We attribute this to the fine resolution of the digital camera image. Subpixel classifier (neural network) is not suitable for classifying aerial digital camera images.