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A CAS500-1 (Compact Advanced Satellite) is a high-resolution Earth-observing satellite launched in Mar 2021 and being operated by National Land Satellite Center at NGII (National Geographic Information Institute). The mission of the satellite is the periodic monitoring of the land for efficient managements. Having completed the calibration and validation processes, CAS500-1 has been acquiring land images to generate the basic geospatial information that are topographic maps, orthorectified images, and DEM (Digital Elevation Model). Using satellite data has a lot of advantages over aerial images in terms of repeatability, coverage, and accessibility. However, the spatial resolution is relatively lower than that of aerial images. This prohibits the large scale topographic mapping from the satellite data. Therefore, we conducted 1:5,000 large scale topographic mappings using CAS500-1 stereo data and assessed the accuracy. Two test areas were selected including accessible and inaccessible areas. Overlapping CAS500-1 data were automatically processed with GCP (Ground Control Points) chips for better positional accuracy. Then they are used for the epipolar resampled images that can be used for stereo-plotting. Through the stereo display quality assessment, we selected the optimal stereo set for each site. The accuracy of the resulted topographic maps was checked using ground truth data such as GNSS (Global Navigation Satellite System)-surveyed points over the accessible area and GCP chips in the inaccessible area, respectively. The experimental results showed the accuracy successfully met the requirement of 1:5,000 topographic map in Korea. We concluded that CAS500-1 data will have more potential for large scale mapping considering that CAS500-2 is scheduled to be launched in 2022 and cooperate with CAS500-1. [ABSTRACT FROM AUTHOR]

Photoclinometry is an important method of reconstructing the topography of planetary surfaces. The technique relies on the photometric response of the surface under known illumination and viewing conditions. Each image pixel embeds the geometric and radiometric information of the surface, thereby allowing the underlying surface to be inferred and reconstructed in full. Photoclinometry is renowned for its ability to reconstruct pixel-wise three-dimensional (3D) models from a single image. However, it is affected by various error sources, including the radiometric quality of the sensor and variations in the surface photometric properties (e.g., albedo). This paper investigated the optimal solar incidence angle for photoclinometric reconstruction using error propagations and simulation analysis, and validated the findings using up-to-date photoclinometric methods and Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) datasets. The results suggested that variations in albedo have a significant effect on the quality of the reconstructed topography. The findings agreed with previous studies, which implies a potential bottleneck in the development of photoclinometric methods. Moreover, the photoclinometric performance becomes stable when the solar incidence angle exceeds 30° and the optimal incidence angle is approximately 60°, depending on the overall topography. Too large an incidence angle results in severe shadows, which hides topographic details, whereas too small an incidence angle introduces errors. The findings enrich the theoretical understanding of photoclinometry and are useful for optimising data capture strategies for future planetary missions to maximise the ability of photoclinometry and provide a foundation for future development of the technique. The recommendations on solar incidence angle and assessment strategies can be directly applied to other airless planetary bodies, such as Mercury and asteroids, for topographic mapping based on photoclinometry. [ABSTRACT FROM AUTHOR]

Abstract: Changes in the spatio-temporal behavior of the brain electrical activity are believed to be associated to epileptic brain states. We propose a novel methodology to identify the different states of the epileptic brain, based on the topographic mapping of the time varying relative power of delta, theta, alpha, beta and gamma frequency sub-bands, estimated from EEG. Using normalized-cuts segmentation algorithm, points of interest are identified in the topographic mappings and their trajectories over time are used for finding out relations with epileptogenic propagations in the brain. These trajectories are used to train a Hidden Markov Model (HMM), which models the different epileptic brain states and the transition among them. Applied to 10 patients suffering from focal seizures, with a total of 30 seizures over 497.3h of data, the methodology shows good results (an average point-by-point accuracy of 89.31%) for the identification of the four brain states – interictal, preictal, ictal and postictal. The results suggest that the spatio-temporal dynamics captured by the proposed methodology are related to the epileptic brain states and transitions involved in focal seizures. [Copyright &y& Elsevier]

Abstract: Generative topographic mapping (GTM) provides a flexible statistical model for unsupervised data inspection and topographic mapping. Since it yields to an explicit mapping of a low-dimensional latent space to the observation space and an explicit formula for a constrained Gaussian mixture model induced thereof, it offers diverse functionalities including clustering, dimensionality reduction, topographic mapping, and the like. However, it shares the property of most unsupervised tools that noise in the data cannot be recognized as such and, in consequence, is visualized in the map. The framework of visualization based on auxiliary information and, more specifically, the framework of learning metrics as introduced in constitutes an elegant way to shape the metric according to auxiliary information at hand such that only those aspects are displayed in distance-based approaches which are relevant for a given classification task. Here we introduce the concept of relevance learning into GTM such that the metric is shaped according to auxiliary class labels. Relying on the prototype-based nature of GTM, efficient realizations of this paradigm are developed and compared on a couple of benchmarks to state-of-the-art supervised dimensionality reduction techniques. [Copyright &y& Elsevier]

Abstract: The generative topographic mapping (GTM) has been proposed as a statistical model to represent high-dimensional data by a distribution induced by a sparse lattice of points in a low-dimensional latent space, such that visualization, compression, and data inspection become possible. The formulation in terms of a generative statistical model has the benefit that relevant parameters of the model can be determined automatically based on an expectation maximization scheme. Further, the model offers a large flexibility such as a direct out-of-sample extension and the possibility to obtain different degrees of granularity of the visualization without the need of additional training. Original GTM is restricted to Euclidean data points in a given Euclidean vector space. Often, data are not explicitly embedded in a Euclidean vector space, rather pairwise dissimilarities of data can be computed, i.e. the relations between data points are given rather than the data vectors themselves. We propose a method which extends the GTM to relational data and which allows us to achieve a sparse representation of data characterized by pairwise dissimilarities, in latent space. The method, relational GTM, is demonstrated on several benchmarks. [Copyright &y& Elsevier]

Abstract: The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or re-mapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSAR-X Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Carajás Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of systematic error (bias) and precision. The test for significant systematic error was based on the Student’s-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuracy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuracy. Thus, the use of TerraSAR-X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available topographic information is rare or presents low quality. [ABSTRACT FROM AUTHOR]

Abstract: A hybrid theoretical–empirical model has been developed for modelling the error in LiDAR-derived digital elevation models (DEMs) of non-open terrain. The theoretical component seeks to model the propagation of the sample data error (SDE), i.e. the error from light detection and ranging (LiDAR) data capture of ground sampled points in open terrain, towards interpolated points. The interpolation methods used for infilling gaps may produce a non-negligible error that is referred to as gridding error. In this case, interpolation is performed using an inverse distance weighting (IDW) method with the local support of the five closest neighbours, although it would be possible to utilize other interpolation methods. The empirical component refers to what is known as “information loss”. This is the error purely due to modelling the continuous terrain surface from only a discrete number of points plus the error arising from the interpolation process. The SDE must be previously calculated from a suitable number of check points located in open terrain and assumes that the LiDAR point density was sufficiently high to neglect the gridding error. For model calibration, data for 29 study sites, 200×200 m in size, belonging to different areas around Almeria province, south-east Spain, were acquired by means of stereo photogrammetric methods. The developed methodology was validated against two different LiDAR datasets. The first dataset used was an Ordnance Survey (OS) LiDAR survey carried out over a region of Bristol in the UK. The second dataset was an area located at Gador mountain range, south of Almería province, Spain. Both terrain slope and sampling density were incorporated in the empirical component through the calibration phase, resulting in a very good agreement between predicted and observed data (). In validation, Bristol observed vertical errors, corresponding to different LiDAR point densities, offered a reasonably good fit to the predicted errors. Even better results were achieved in the more rugged morphology of the Gador mountain range dataset. The findings presented in this article could be used as a guide for the selection of appropriate operational parameters (essentially point density in order to optimize survey cost), in projects related to LiDAR survey in non-open terrain, for instance those projects dealing with forestry applications. [Copyright &y& Elsevier]

Abstract: Dopaminergic neurons located in the ventral midbrain (i.e. mesodiencephalic dopamine, mdDA, neurons) are essential for the control of diverse cognitive and motor behaviors and are associated with multiple psychiatric and neurodegenerative disorders. Three anatomically and functionally distinct subgroups of mdDA neurons have been identified (A8–A10) which give rise to prominent forebrain projections (i.e. the mesostriatal, mesocortical and mesolimbic pathways). The development of mdDA neurons is a complex, multi-step process. It includes early developmental events such as cell fate specification, differentiation and migration, and later events including neurite growth, guidance and pruning, and synapse formation. Significant progress has been made in defining the early events involved in mdDA neuron development [see Smits, S.M., Burbach, J.P., Smidt, M.P., 2006. Developmental origin and fate of meso-diencephalic dopamine neurons. Prog. Neurobiol. 78, 1–16.]. Although later stages of mdDA neuron development are less well understood, recent studies have begun to identify cellular and molecular signals thought to be involved in establishing mdDA neuronal connectivity. The purpose of the present review is to summarize our current understanding of the ontogeny and anatomy of mdDA axon pathways, to highlight recent progress in defining the cellular and molecular mechanisms that underlie the formation and remodeling of mdDA circuits, and to discuss the significance of this progress for understanding and treating situations of perturbed connectivity in the mdDA system. [Copyright &y& Elsevier]

Abstract: In the 2003 Mars Exploration Rover (MER) mission, the twin rovers, Spirit and Opportunity, carry identical Athena instrument payloads and engineering cameras for exploration of the Gusev Crater and Meridiani Planum landing sites. This paper presents the photogrammetric processing techniques for high accuracy topographic mapping and rover localization at the two landing sites. Detailed discussions about camera models, reference frames, interest point matching, automatic tie point selection, image network construction, incremental bundle adjustment, and topographic product generation are given. The developed rover localization method demonstrated the capability of correcting position errors caused by wheel slippages, azimuthal angle drift and other navigation errors. A comparison was also made between the bundle-adjusted rover traverse and the rover track imaged from the orbit. Mapping products including digital terrain models, orthophotos, and rover traverse maps have been generated for over two years of operations, and disseminated to scientists and engineers of the mission through a web-based GIS. The maps and localization information have been extensively used to support tactical operations and strategic planning of the mission. [Copyright &y& Elsevier]

Abstract: High-resolution Cassini stereo images of Saturn''s moon Phoebe have been used to derive a regional digital terrain model (DTM) and an orthoimage mosaic of the surface. For DTM-control a network of 130 points measured in 14 images (70–390m/pixel resolution) was established which was simultaneously used to determine the orientation of the spin-axis. The J2000 spin-axis was found at Dec=78.0°±0.1° and RA=356.6°±0.3°, substantially different from the former Voyager solution. The control points yield a mean figure radius of 107.2km with RMS residuals of 6.2km demonstrating the irregular shape of this body. The DTM was computed from densely spaced conjugate image points determined by methods of digital image correlation. It has a horizontal resolution of 1–2km and vertical accuracies in the range 50–100m. It is limited in coverage, but higher in resolution than the previously derived global shape model of Phoebe [Porco et al., 2005. Cassini imaging science: initial results on Phoebe and Iapetus. Science 307, 1237–1242] and allows us to study the morphology of the surface in more detail. There is evidence for unconsolidated material from a steep and smooth slope at the rim of a 100km impact feature. There are several conically shaped craters on Phoebe, which may hint at highly porous and low compaction material on the surface. [Copyright &y& Elsevier]

Abstract: We use airborne lidar data for the summit area of Kilauea Caldera, Hawaii, to explore the utility of topographic data collected by the TOPSAR airborne interferometric radar for volcanology studies. The lidar data are processed to a spatial resolution of 1 m/pixel, compared to TOPSAR with a spatial resolution of 5 m. Over a variety of fresh volcanic surfaces (pahoehoe and aa lava flows, ash falls and fluvial fans), TOPSAR data are shown to have a typical vertical offset compared to the lidar data of no more than ∼2–3 m. Larger differences between the two data sets and TOPSAR data drop-outs are found to be concentrated around steep scarps such as the walls of pit craters and ground cracks associated with the Southwest Rift Zone. A comparison of these two data sets is used to explore the utility of TOPSAR to interpret the topography of volcanic features close to the spatial resolution of TOPSAR, such as spatter ramparts, fractures, a perched lava flow, and eroded ash deposits. Comparison of the TOPSAR elevation and the lidar first-return minus the return from the ground surface (the so-called “bald Earth” data) for vegetated areas reveals TOPSAR penetration into the tree canopy is typically at least 10% and no more than ∼50%, although a wide range of penetration values from 0% to 90% has been identified. Our results are significant because they show that TOPSAR data for volcanoes can reliably be used to measure regional slopes and the thickness of lava flows, and have value for the validation of coarser spatial resolution digital elevation data (such as SRTM) in areas where lidar data have not been collected. [Copyright &y& Elsevier]

Abstract: Here, an multidimensional scaling-based (MDS-based) topographic mapping algorithm is proposed, named the stochastic MDS network. Because this network utilizes not local but global information over all the units, it can find more optimal results than previous models. In addition, by using a stochastic gradient algorithm, the mapping formation in this network is carried out as efficiently as in SOM-like models based on only the local information. Some simple numerical experiments verified the validity and efficiency of this network. It was also applied to the formation of large-scale topographic mappings, and could form various interesting mappings. [Copyright &y& Elsevier]

Abstract: We propose a simple topographic mapping formation model between cell layers with weight normalization. In our model, each cell layer can have an arbitrary neighborhood relation between the cells represented by an undirected graph. Thus, a topographic mapping described in this model is a map which preserves the adjacency relation. We define several learning rules, input and output type weight normalization methods. Then, we not only concentrate on a Hebbean weight modification but also investigate the effects of normalization under a non-Hebbean weight modification. We first show that when an input type normalization is adopted or without normalization, a topographic mapping is stable under the correlational type learning rule, but when an output type normalization is adopted a topographic mapping is stable under not only the correlational type learning rule but also the non-correlational one. Next, we show by computer simulations that when an output type normalization is considered we have more learning rules which yield topographic mappings than the cases when an input type normalization is adopted or without normalization. [Copyright &y& Elsevier]

Abstract: We propose a simple topographic mapping formation model from a cell layer to a cell layer. Our model is a discrete one in that the state value of input and output cells takes 0 or 1 and input and output layers are represented by undirected graphs. A binary input pattern can be given to the network consisting of input and output cell layers. Such an input pattern can be represented by a subset of input cells. That is, a state value of an input cell takes 1 if a cell belongs to the subset, otherwise, a state value of an input cell is 0. Such a definition of an input pattern does not necessarily assume a short-range excitatory mechanism in an input layer. Thus, a topographic mapping described in this model is a map, which preserves the input pattern relation. By using the concept of input pattern separability, we showed an existence condition of certain learning rules, which are correlational. We have paid special attention to such correlational type learning rules, and have shown under the rules that topographic mappings are the only stable ones. As to the non-correlational learning rules, we also investigate the stability of generated mappings. [Copyright &y& Elsevier]

Self-organizing maps (SOMs) are widely used in several fields of application, from neurobiology to multivariate data analysis. In that context, this paper presents variants of the classic SOM algorithm. With respect to the traditional SOM, the modifications regard the core of the algorithm, (the learning rule), but do not alter the two main tasks it performs, i.e. vector quantization combined with topology preservation. After an intuitive justification based on geometrical considerations, three new rules are defined in addition to the original one. They develop interesting properties such as recursive neighborhood adaptation and non-radial neighborhood adaptation. In order to assess the relative performances and speeds of convergence, the four rules are used to train several maps and the results are compared according to several error measures (quantization error and topology preservation criterions). [Copyright &y& Elsevier]