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Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Microplastic (MP) contamination has become a problem of great interest to the community at large. The detection of these particles in different ecosystems and foods has been the subject of study. However, the focus of these investigations has been on the identification and quantification of PM by DSC and Pyr-GC/MS and not on how they are transported to reach the air we breathe. In this study, the values of morphological parameters for plastic particles in a range between 1 and 2000 µm, present in the breathable air of 20 neighborhoods in the city of Cartagena, Colombia, were obtained to determine the characteristics that make these particles airborne. The values of parameters were obtained, such as roundness, sphericity, curvature, and the convexity of the particle, as well as its compactness and size, which influence its transport through the air and its ability to be ingested and inhaled. The data obtained in this study allows for simulations and the analysis of the behavior of microplastics once in the environment to predict future settlements. The DSC showed us the melting temperatures of PP, PE, PET, and PS, the Pyr-GC/MS showed the fragmentation patterns, and the presence of these MPs in the samples was confirmed. [ABSTRACT FROM AUTHOR]

Abstract Background Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results. Results Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box–Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions. Conclusions Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less.

Hyperspectral sensors, due to the acquisition of a large number of spectral bands, always have particular importance in monitoring the phenomena of the earth's surface. The classification of hyperspectral images is the most crucial method of processing hyperspectral data, and many efforts have been made to increase its accuracy. In recent years, convolutional neural networks (CNNs) and spatial features have been a big part of how well hyperspectral images can be classified. CNNs, due to the automatic generation of features and reduction of parameters, compared to multilayer perceptron neural networks by sharing parameters in each layer, have received much attention from researchers in the field of pattern recognition. In previous research, much attention has not been paid to the simultaneous use of spatial feature extraction methods in CNNs. For this reason, in this paper, a new CNNs architecture has been introduced to classify hyperspectral images, which uses the spectral-spatial vector obtained from the morphological profiles method as the network's input. Morphological profiles, which include a set of opening and closing filters, are generally applied to the components of the image that contain the most information. In this research, non-linear principal component analysis (non-linear PCA) implemented by neural networks was used to summarize hyperspectral image information. Therefore, the main innovation of this paper is to provide a three-stage framework for applying deep learning. The first step is to reduce the dimensions of the hyperspectral image using non-linear PCA, the second stage is to extract spatial features using the morphological profiles method, and the third step is to prepare the inputs and design the CNNs architecture. The proposed method was evaluated on three benchmark hyperspectral images of Pavia, Berlin and Telops. The results of the experiments show the superiority of the proposed method by 13, 11 and 17% in the overall accuracy parameter compared to the support vector machines (SVM) classification algorithm in the Pavia, Berlin and Telops images, respectively. [ABSTRACT FROM AUTHOR]

Background: Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results. Results: Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box–Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions. Conclusions: Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less. [ABSTRACT FROM AUTHOR]

Due to abundant spectral information contained in the hyperspectral images, they are suitable data for anomalous targets detection. The use of spatial features in addition to spectral ones can improve the anomaly detection performance. An anomaly detector, called nonparametric spectral-spatial detector (NSSD), is proposed in this work which utilizes the benefits of spatial features and local structures extracted by the morphological filters. The obtained spectral-spatial hypercube has high dimensionality. So, accurate estimates of the background statistics in small local windows may not be obtained. Applying conventional detectors such as Local Reed Xiaoli (RX) to the high dimensional data is not possible. To deal with this difficulty, a nonparametric distance, without any need to estimate the data statistics, is used instead of the Mahalanobis distance. According to the experimental results, the detection accuracy improvement of the proposed NSSD method compared to Global RX, Local RX, weighted RX, linear filtering based RX (LF-RX), background joint sparse representation detection (BJSRD), Kernel RX, subspace RX (SSRX) and RX and uniform target detector (RX-UTD) in average is 47.68%, 27.86%, 13.23%, 29.26%, 3.33%, 17.07%, 15.88%, and 44.25%, respectively.

Rapid identification of post-earthquake collapsed buildings can be conducive to capturing an immediate disaster assessment, which can help design effective emergency response strategies. At present, earthquake disaster assessment in practice mainly relies on time-consuming artificial field investigations, which cannot adapt to the demands of a timely rescue. However, morphological methods have proved to be of great potential in describing the disaster characteristics of building as for size, shape, directionality and contrast. Conventionally, boundaries of intact building appear flat and smooth, thus it can be reasonably depicted by linear structural element, while collapsed buildings generally emerge dispersedly distributed without regular geometry which can be suitably described by disk structural element. Based on these intuitive findings, we propose an approach for extracting intact and collapsed buildings via multi-scale morphological profiles with multi-structuring elements from post-earthquake satellite imagery. This approach consists of three core components: 1) Linear and disk structuring elements are established through top-hat reconstruction for extracting the initial intact and collapsed building, respectively. 2) Purified intact and collapsed buildings can be obtained by a straightforward threshold segmentation and further post processing, such as area, normalized green plant index (NGPI) and the length-width ratio for image noise suppression. 3) Boolean set operations are adopted to distinguish the intact and collapsed buildings. Experiments demonstrate that the proposed method has achieved satisfactory results and exerted great superiority in computational efficiency.

Microplastic (MP) contamination has become a problem of great interest to the community at large. The detection of these particles in different ecosystems and foods has been the subject of study. However, the focus of these investigations has been on the identification and quantification of PM by DSC and Pyr-GC/MS and not on how they are transported to reach the air we breathe. In this study, the values of morphological parameters for plastic particles in a range between 1 and 2000 µm, present in the breathable air of 20 neighborhoods in the city of Cartagena, Colombia, were obtained to determine the characteristics that make these particles airborne. The values of parameters were obtained, such as roundness, sphericity, curvature, and the convexity of the particle, as well as its compactness and size, which influence its transport through the air and its ability to be ingested and inhaled. The data obtained in this study allows for simulations and the analysis of the behavior of microplastics once in the environment to predict future settlements. The DSC showed us the melting temperatures of PP, PE, PET, and PS, the Pyr-GC/MS showed the fragmentation patterns, and the presence of these MPs in the samples was confirmed.

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less., Competing Interests: There are no competing financial interests., (© 2024 The Authors.)

A nonparametric anomaly detection method is proposed in this paper which does not consider any probability density function for data, and so can work well for real hyperspectral image containing complicated and non-normal background. Since the predominant part of an image is composed by background pixels and because of similarity of neighboring pixels in a local region, each background pixel can be approximated from its surrounding samples. To this end, the collaborative representation with a simple and closed form solution is used. To more benefit from the spatial information of hyperspectral image, the morphological filters are applied for extraction of contextual features and a decision fusion rule is used to utilize the spatial information of all principal components of data. The experimental results show the superior performance of the proposed detector compared to several state-of-the-art anomalous target detection methods. The area under ROC curve (AUC) values achieved by the proposed detector are 95.53, 96.34 and 99.72 for San Diego hyperspectral image, Indian hyperspectral image and the WorldView-2 multispectral image, respectively.

Anomaly detection is one of the most challenging topics in hyperspectral imaging due to the high spectral resolution of the images and the lack of spatial and spectral information about the anomaly. In this paper, a novel hyperspectral anomaly detection method called morphological profile and attribute filter (MPAF) algorithm is proposed. Aiming to increase the detection accuracy and reduce computing time, it consists of three steps. First, select a band containing rich information for anomaly detection using a novel band selection algorithm based on entropy and histogram counts. Second, remove the background of the selected band with morphological profile. Third, filter the false anomalous pixels with attribute filter. A novel algorithm is also proposed in this paper to define the maximum area of anomalous objects. Experiments were run on real hyperspectral datasets to evaluate the performance, and analysis was also conducted to verify the contribution of each step of MPAF. The results show that the performance of MPAF yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), precision-recall, and computing time, i.e., 0.9916, 0.7055, and 0.25 s, respectively. Compared with four other anomaly detection algorithms, MPAF yielded the highest average AUC for ROC and precision-recall in eight out of thirteen and nine out of thirteen datasets, respectively. Further analysis also proved that each step of MPAF has its effectiveness in the detection performance.

A nonparametric anomaly detection method is proposed in this paper which does not consider any probability density function for data, and so can work well for real hyperspectral image containing complicated and non-normal background. Since the predominant part of an image is composed by background pixels and because of similarity of neighboring pixels in a local region, each background pixel can be approximated from its surrounding samples. To this end, the collaborative representation with a simple and closed form solution is used. To more benefit from the spatial information of hyperspectral image, the morphological filters are applied for extraction of contextual features and a decision fusion rule is used to utilize the spatial information of all principal components of data. The experimental results show the superior performance of the proposed detector compared to several state-of-the-art anomalous target detection methods. The area under ROC curve (AUC) values achieved by the proposed detector are 95.53, 96.34 and 99.72 for San Diego hyperspectral image, Indian hyperspectral image and the WorldView-2 multispectral image, respectively. [ABSTRACT FROM AUTHOR]

A new morphological attribute building index (MABI) and shadow index (MASI) are proposed here for automatically extracting building features from very high-resolution (VHR) remote sensing satellite images. By investigating the associated attributes in morphological attribute filters (AFs), the proposed method establishes a relationship between AFs and the characteristics of buildings/shadows in VHR images (e.g., high local contrast, internal homogeneity, shape, and size). In the pre-processing step of the proposed work, attribute filtering was conducted on the original VHR spectral reflectance data to obtain the input, which has a high homogeneity, and to suppress elongated objects (potential non-buildings). Then, the MABI and MASI were calculated by taking the obtained input as a base image. The dark buildings were considered separately in the MABI to reduce the omission of the dark roofs. To better detect buildings from the MABI feature image, an object-oriented analysis and building-shadow concurrence relationships were utilized to further filter out non-building land covers, such as roads and bare ground, that are confused for buildings. Three VHR datasets from two satellite sensors, i.e., Worldview-2 and QuickBird, were tested to determine the detection performance. In view of both the visual inspection and quantitative assessment, the results of the proposed work are superior to recent automatic building index and supervised binary classification approach results.

The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although it was originally designed for temperature study, the morphological structure concealed in LCZs also reflects economic status and population distribution. To this end, global LCZ classification is of great value for worldwide studies on economy and population. Conventional classification approaches are usually successful for an individual city using optical remote sensing data. This paper, however, attempts for the first time to produce global LCZ classification maps using polarimetric synthetic aperture radar (PolSAR) data. Specifically, we first produce polarimetric features, local statistical features, texture features, and morphological features and compare them, with respect to their classification performance. Here, an ensemble classifier is investigated, which is trained and tested on already separated transcontinental cities. Considering the challenging global scope this work handles, we conclude the classification accuracy is not yet satisfactory. However, Sentinel-1 dual-Pol SAR data could contribute the classification for several LCZ classes. According to our feature studies, the combination of local statistical features and morphological features yields the best classification results with 61.8% overall accuracy (OA), which is 3% higher than the OA produced by the second best features combination. The 3% is considerably large for a global scale. Based on our feature importance analysis, features related to VH polarized data contributed the most to the eventual classification result.

Participated in the XXIII Brazilian Championship of Powerlifting held in Brusque, Santa Catarina. Fifty-one athletes (38 men and 13 women) of different age groups participated in anthropometric measuring of skinfolds (DC), body circumferences (PC), bone diameters, body mass (MC) and stature. Body composition was defined by the two-component model. The athletes were measured either one or two days before the competition, all of them were volunteers and they represented various Brazilian regions. They had been classified in selective competitions in their home states. Results pointed out that muscle mass was smaller than that observed in international weightlifters, while their body fat levels were higher. Among female athletes, body fat was more prominent in the extremities, especially in the lower limbs. The male athletes presented an even distribution of body fat in extremities and trunk. In conclusion, Brazilian powerlifters presented values of body components that diverge from their international counterparts, particularly for DC and PC as a function of age range. . RESUMO Este estudo teve como objetivo traçar características morfológicas de atletas homens e mulheres de diversas faixas etárias da elite do levantamento de potência nacional que participaram do XXIII Campeonato Brasileiro de Powerlifting realizado em Brusque (Santa Catarina). Para tal 51 atletas (38 homens e 13 mulheres) de levantamento de potência participaram de mensurações antropométricas de dobras cutâneas (DC), perímetros corporais (PC), diâmetros ósseos, massa corporal (MC) e estatura. Também foram determinados valores da composição corporal em dois componentes. Os atletas foram avaliados de um a dois dias antes da competição sendo todos voluntários e procedentes de várias regiões do Brasil tendo sido classificados em seletivas estaduais. Os resultados apontaram o tecido magro abaixo do verificado em atletas internacionais de potência, porém com tecido gordo acima destes, sendo que, nas atletas mulheres esta gordura é mais proeminente nas extremidades corporais apresentando-se mais intensa nos membros inferiores. Os atletas apresentaram uma distribuição de gordura equilibrada entre os segmentos e tronco. Conclui-se que os atletas brasileiros de powerlifting apresentam magnitude dos componentes corporais que divergem dos internacionais com peculiaridades nos valores de DC e PC em função da faixa etária.

The present study was undertaken to evaluate the seeds morphology of Sida cordifolia L. collected from different localities (Kothi Compound, Civil Lines, A. G. College Campus and Kuthuliya) of district rewa. Sida cordifolia seeds are reniform, about 1.82 mm long and 1.31mm broad, yellowish brown in colour. The average weight and volume of seeds were noted to be 12.06 mg and 0.97 cc respectively. The maximum seed output capacity was recorded 588.31 in A. G. College Campus and minimum seed output capacity was recorded 403.56 in Kuthuliya (Bichhiya). [ABSTRACT FROM AUTHOR]

Image segmentation is a key and prerequisite step for object-based analysis of very high resolution (VHR) imagery. Most existing image segmentation methods use either spectral or spatial information of an image alone. A novel image segmentation method for VHR multispectral images using combined spectral and morphological information is proposed in this paper. The method can be summarized as follows. First, a morphological derivative profile is calculated from an original multispectral image and combined with the spectral bands to quantify spectral-morphological characteristics of a pixel, which are considered as a criterion of homogeneity of neighboring pixels. Image segmentation is then conducted using a seeded region-growing procedure, which is based on the seed points automatically generated from the gradient image and dynamically added and the similarity between a seed pixel and its neighboring pixels in terms of spectral-morphological characteristics. The obtained segmentation result is further refined by a region merging procedure to generate a final segmentation result. The proposed method is evaluated using three VHR images of urban and suburban areas and compared with two existing segmentation methods, in terms of visual inspection, quantitative evaluation and indirect evaluation. Experimental results demonstrate that the joint use of spectral and morphological information outperformed the use of morphological information alone. Furthermore, the proposed image segmentation method performed better than existing methods. The proposed image segmentation method is well applicable to the segmentation of VHR imagery over urban and suburban areas. [ABSTRACT FROM AUTHOR]

The article is focused on the analysis of possibilities of using granulometric analysis methods: the morphological profile, and granulometric maps in detecting buildings on satellite images. It briefly explains the theoretical basis for granulometric analysis of image and compares two methods used in research. Tests were carried out on a fragment of QuickBird satellite scene - pansharpened multispectral image. 8 variants of classification differing in terms of the data and the model of classification were compared. Evaluation of the effectiveness of the different options for classification based on the analysis factor kappa values and omission and commission errors. The results indicate the significant potential of the proposed methods, and analysis of the observed imperfections allows to specify the possible fields of their development. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple unsupervised framework to effectively select and combine spectral information and spatial features for Support Vector Machine (SVM)-based classification when spatial features are the widely used morphological profiles (MPs). To overcome the difficulty of high dimensionality of resulting features, it is a common practice that MPs are extracted from principal components (PCs). In this paper, we investigate another technique on spectral feature selection, which is unsupervised band selection (BS). We find out that using selected bands as spectral features can improve classification performance because they contain more critical characteristics for classification; in particular, using the selected bands, combined with the MPs extracted from PCs, can yield the highest accuracy, due to the fact that major PCs contain less noise for extracting more reliable MPs. The overall unsupervised nature of feature selection provides the flexibility of implementation. We believe that such finding is instructive to feature selection and extraction for spectral/spatial-based hyperspectral image classification. [ABSTRACT FROM PUBLISHER]

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The classification of high resolution urban remote sensing imagery is addressed with the focus on classification of imagery that has been pansharpened by a number of different pansharpening methods. The pansharpening process introduces some spectral and spatial distortions in the resulting fused multispectral image, the amount of which highly varies depending on which pansharpening technique is used. In the majority of the pansharpening techniques that have been proposed, there is a compromise between the spatial enhancement and the spectral consistency. Here we study the effects of the spectral and spatial distortions on the accuracy in classification of pansharpened imagery. We also study the performance in terms of accuracy of the various pansharpening techniques during classification with spatial information, obtained using mathematical morphology (MM). MM is used to derive local spatial information from the panchromatic data. Random Forests (RF) and Support Vector Machines (SVM) will be used as classifiers. Experiments are done for three different datasets that have been obtained by two different imaging sensors, IKONOS and QuickBird. These sensors deliver multispectral images that have four bands, R, G, B and near infrared (NIR). To further study the contribution of the NIR band, experiments are done using both the RGB bands and all four bands, respectively. [ABSTRACT FROM PUBLISHER]

The state of built-up features after their destruction, as well as the process of their rehabilitation, are assessed through the analysis of conflict and postconflict very high spatial resolution Ikonos images using a pixel-level support vector machine (SVM) learning classification approach. Different input vectors of the supervised SVM classifier are tested in order to assess the discrimination power of structural and spectral image descriptors: the use of spectral information only with (a) the panchromatic images at time t0 and t1, (b) the pan-sharpened images with the multispectral bands at time t0 and t1, (c) the iteratively re-weighted multivariate alteration detection (IR-MAD) variates derived from dataset (b); the use of structural information only with image series resulting from the decomposition by the derivative of the morphological profile (DMP) of the panchromatic (d) and pan-sharpened (e) data; finally, the use of spectral and structural information simultaneously (f) and (g) by stacking up (a) and (d), and (b) and (e), respectively. The results show that the SVM performs better with feature vectors based on the simultaneous use of spectral and structural information rather than with those formed by the grey-level information or the DMPs only. Moreover, approach (f) requiring only two panchromatic data as input compete well with approaches (b), (e), and (g), which instead necessitate ten spectral channels as input. [ABSTRACT FROM PUBLISHER]

Anomaly detection is one of the most challenging topics in hyperspectral imaging due to the high spectral resolution of the images and the lack of spatial and spectral information about the anomaly. In this paper, a novel hyperspectral anomaly detection method called morphological profile and attribute filter (MPAF) algorithm is proposed. Aiming to increase the detection accuracy and reduce computing time, it consists of three steps. First, select a band containing rich information for anomaly detection using a novel band selection algorithm based on entropy and histogram counts. Second, remove the background of the selected band with morphological profile. Third, filter the false anomalous pixels with attribute filter. A novel algorithm is also proposed in this paper to define the maximum area of anomalous objects. Experiments were run on real hyperspectral datasets to evaluate the performance, and analysis was also conducted to verify the contribution of each step of MPAF. The results show that the performance of MPAF yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), precision-recall, and computing time, i.e., 0.9916, 0.7055, and 0.25 s, respectively. Compared with four other anomaly detection algorithms, MPAF yielded the highest average AUC for ROC and precision-recall in eight out of thirteen and nine out of thirteen datasets, respectively. Further analysis also proved that each step of MPAF has its effectiveness in the detection performance. [ABSTRACT FROM AUTHOR]

A new morphological attribute building index (MABI) and shadow index (MASI) are proposed here for automatically extracting building features from very high-resolution (VHR) remote sensing satellite images. By investigating the associated attributes in morphological attribute filters (AFs), the proposed method establishes a relationship between AFs and the characteristics of buildings/shadows in VHR images (e.g., high local contrast, internal homogeneity, shape, and size). In the pre-processing step of the proposed work, attribute filtering was conducted on the original VHR spectral reflectance data to obtain the input, which has a high homogeneity, and to suppress elongated objects (potential non-buildings). Then, the MABI and MASI were calculated by taking the obtained input as a base image. The dark buildings were considered separately in the MABI to reduce the omission of the dark roofs. To better detect buildings from the MABI feature image, an object-oriented analysis and building-shadow concurrence relationships were utilized to further filter out non-building land covers, such as roads and bare ground, that are confused for buildings. Three VHR datasets from two satellite sensors, i.e., Worldview-2 and QuickBird, were tested to determine the detection performance. In view of both the visual inspection and quantitative assessment, the results of the proposed work are superior to recent automatic building index and supervised binary classification approach results. [ABSTRACT FROM AUTHOR]

The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although it was originally designed for temperature study, the morphological structure concealed in LCZs also reflects economic status and population distribution. To this end, global LCZ classification is of great value for worldwide studies on economy and population. Conventional classification approaches are usually successful for an individual city using optical remote sensing data. This paper, however, attempts for the first time to produce global LCZ classification maps using polarimetric synthetic aperture radar (PolSAR) data. Specifically, we first produce polarimetric features, local statistical features, texture features, and morphological features and compare them, with respect to their classification performance. Here, an ensemble classifier is investigated, which is trained and tested on already separated transcontinental cities. Considering the challenging global scope this work handles, we conclude the classification accuracy is not yet satisfactory. However, Sentinel-1 dual-Pol SAR data could contribute the classification for several LCZ classes. According to our feature studies, the combination of local statistical features and morphological features yields the best classification results with 61.8% overall accuracy (OA), which is 3% higher than the OA produced by the second best features combination. The 3% is considerably large for a global scale. Based on our feature importance analysis, features related to VH polarized data contributed the most to the eventual classification result. [ABSTRACT FROM AUTHOR]