Tinghua Ai

A spatial and temporal reflectance fusion model considering sensor observation differences

This article proposes a spatial–temporal expansion method for remote-sensing reflectance by blending observations from sensors with different spatial and temporal characteristics. Compared with the methods used in the past, the main characteristic of the proposed method is consideration of sensor observation differences between different cover types when calculating the weight function of the fusion model. The necessity of the temporal difference factor commonly used in spatial–temporal fusion is also analysed in this article. The method was tested and quantitatively assessed under different landscape situations. The results indicate that the proposed fusion method improves the prediction accuracy of fine-resolution reflectance.

Building pattern recognition in topographic data: examples on collinear and curvilinear alignments

Building patterns are important features that should be preserved in the map generalization process. However, the patterns are not explicitly accessible to automated systems. This paper proposes a framework and several algorithms that automatically recognize building patterns from topographic data, with a focus on collinear and curvilinear alignments. For both patterns two algorithms are developed, which are able to recognize alignment-of-center and alignment-of-side patterns. The presented approach integrates aspects of computational geometry, graph-theoretic concepts and theories of visual perception. Although the individual algorithms for collinear and curvilinear patterns show great potential for each type of the patterns, the recognized patterns are neither complete nor of enough good quality. We therefore advocate the use of a multi-algorithm paradigm, where a mechanism is proposed to combine results from different algorithms to improve the recognition quality. The potential of our method is demonstrated by an application of the framework to several real topographic datasets. The quality of the recognition results are validated in an expert survey.

Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Land-surface temperature (LST) is of great significance for the estimation of radiation and energy budgets associated with land-surface processes. However, the available satellite LST products have either low spatial resolution or low temporal resolution, which constrains their potential applications. This paper proposes a spatiotemporal fusion method for retrieving LST at high spatial and temporal resolutions. One important characteristic of the proposed method is the consideration of the sensor observation differences between different land-cover types. The other main contribution is that the spatial correlations between different pixels are effectively considered by the use of a variation-based model. The method was tested and assessed quantitatively using the different sensors of Landsat TM/ETM+, moderate resolution imaging spectroradiometer and the geostationary operational environmental satellite imager. The validation results indicate that the proposed multisensor fusion method is accurate to about 2.5 K.

Road selection based on Voronoi diagrams and strokes in map generalization

Abstract Road selection is a prerequisite to effective road network generalization. This article introduces a novel algorithm for road network selection in map generalization, which take four types of information into consideration: statistical, metric, topological, and thematic at three spatial scales: macro-scale which describes the general pattern of networks, mezzo-scale that handles relationships among road segments, and micro-scale that focuses on individual roads’ properties. A set of measures is selected to quantify these different types of information at various spatial levels. An algorithm is then developed with the extraction of these measures based on Voronoi diagrams and a perceptual grouping method called “stroke”. The selection process consists of three consecutive steps: measuring network information based on Voronoi partitioning and stroke generation, selecting roads based on information extraction in the first step with strokes as selection unit, and assessing selection results. The algorithm is further tested with a real-world dataset: road network map at 1:10,000 scale and its generalized version at 1:50,000 scale in Wuhan, China. The result reveals that the algorithm can produce reasonable selection results and thus has the potential to be adopted in road selection in map generalization.

Automated evaluation of building alignments in generalized maps

Evaluation is a key step to examine the quality of generalized maps with respect to map requirements. Map generalization facilitates the recognition of pattern generating processes by preserving and highlighting the patterns at smaller scales. This article focuses specifically on the evaluation of building patterns in topographic maps that are generalized from large to mid scales. Currently, there is a lack of knowledge and functionality on automatically evaluating how these patterns are generalized. The issues of the evaluation range from missing formal map requirements on building alignments to missing automated evaluation techniques. This article firstly analyses the requirements (constraints) related to the generalization of building alignments. Then, it focuses on three more specific constraints, i.e. on existence, orientation of alignments and spatial distribution of composing buildings. Later, a three-step approach is proposed to (1) recognize and (2) match alignments from source and generalized datasets and (3) evaluate building alignments in generalized datasets. Besides, many-to-many and partial matching between initial and target alignments is a side effect of generalization, which reduces the reliability of the evaluation results. This article introduces a confidence indicator to document the reliability and to inform intended users (e.g. cartographers) and/or systems about the reliability of evaluation decisions. The effectiveness of our approach is demonstrated by evaluating the alignments in both interactively (manually) generalized maps and automated generalized maps. Finally, we discuss how our approach can be used to control automated generalization and identify further improvements.

Characterization and Detection of Building Patterns in Cartographic Data: Two Algorithms

Building patterns are important settlement structures in applications like automated generalization and spatial data mining. Previous investigations have focused on a few types of building patterns (e.g. collinear building alignments); while many other types are less discussed. In order to get better known of the building patterns available in geography, this paper studies existing topographic maps at large to medium scales, and proposes and discusses a comprehensive typology of building patterns, their distinctions and characteristics. The proposed typology includes linear alignments (i.e. collinear, curvilinear, align-along-road alignments) and nonlinear clusters (grid-like and unstructured patterns). We concentrate in this paper on two specific building structures: align-along-road alignment and unstructured clusters. Two graph-theoretic algorithms are presented to detect these two types of building patterns. The approach bases itself on auxiliary data structures such as Delaunay triangulation and minimum spanning trees for clustering; several rules are used to refine the clusters into specific building patterns. Finally, the proposed algorithms are tested against a real topographic dataset of the Netherlands, which shows the potential of the two algorithms.

Envelope generation and simplification of polylines using Delaunay triangulation

ABSTRACT As a basic and significant operator in map generalization, polyline simplification needs to work across scales. Perkal’s ε-circle rolling approach, in which a circle with diameter ε is rolled on both sides of the polyline so that the small bend features can be detected and removed, is considered as one of the few scale-driven solutions. However, the envelope computation, which is a key part of this method, has been difficult to implement. Here, we present a computational method that implements Perkal’s proposal. To simulate the effects of a rolling circle, Delaunay triangulation is used to detect bend features and further to construct the envelope structure around a polyline. Then, different connection methods within the enveloping area are provided to output the abstracted result, and a strategy to determine the best connection method is explored. Experiments with real land-use polygon data are implemented, and comparison with other algorithms is discussed. In addition to the scale-specificity inherited from Perkal’s proposal, the results show that the proposed algorithm can preserve the main shape of the polyline and meet the area-maintaining constraint during large-scale change. This algorithm is also free from self-intersection.

A vector field model to handle the displacement of multiple conflicts in building generalization

In map generalization, the displacement operation attempts to resolve proximity conflicts to guarantee map legibility. Owing to the limited representation space, conflicts may occur between both the same and different features under different contexts. A successful displacement should settle multiple conflicts, suppress the generation of secondary conflicts after moving some objects, and preserve the distribution patterns. The effect of displacement can be understood as a force that pushes related objects away with properties of propagation and distance decay. This study borrows the idea of vector fields from physics discipline and establishes a vector field model to handle the displacement of multiple conflicts in building generalization. A scalar field is first constructed based on a Delaunay triangulation skeleton to partition the buildings being examined (e.g., a street block). Then, we build a vector field to conduct displacement measurements through the detection of conflicts from multiple sources. The direction and magnitude of the displacement force are computed based on an iso-line model of vector field. The experiment shows that this global method can settle multiple conflicts and preserve the spatial relations and important building patterns.

Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects

ABSTRACT The aim of mining spatial co-location patterns is to find the corresponding subsets of spatial features that have strong spatial correlation in the real world. This is an important technology for the extraction and comprehension of implicit knowledge in large spatial databases. However, existing methods of co-location mining consider events as taking place in a homogeneous and isotropic context in Euclidean space, whereas the physical movement in an urban space is usually constrained by a road network. Furthermore, previous works do not take the ‘distance decay effect’ of spatial interactions into account, which may reduce the effectiveness of the result. Here we propose an improved spatial co-location pattern mining method, including the network-constrained neighborhood and addition of a distance-decay function, to find the spatial dependence between network phenomena (e.g. urban facilities). The underlying idea is to utilize a model function in the interest measure calculation to weight the contribution of a co-location to the overall interest measure instance inversely proportional to the separation distance. Our approach was evaluated through extensive experiments using facility points-of-interest data sets. The results show that the network-constrained approach is a more effective method than the traditional one in network-structured space. The proposed approach can also be applied to other human activities (e.g. traffic accidents) constrained by a street network.

Universal reconstruction method for radiometric quality improvement of remote sensing images

The performance of remote sensing images in some applications is often affected by the existence of noise, blurring, stripes and corrupted pixels, as well as the hardware limits of the sensor with respect to spatial resolution. This paper presents a universal reconstruction method that can be used to improve the image quality by performing image denoising, deconvolution, destriping, inpainting, interpolation and super-resolution reconstruction. The proposed method consists of two parts: a universal image observation model and a universal image reconstruction model. In the observation model, most degradation processes in remote sensing imaging are considered in order to relate the desired image to the observed images. For the reconstruction model, we use the maximum a posteriori (MAP) framework to set up the minimization energy equation. The likelihood probability density function (PDF) is constructed based on the image observation model, and a robust Huber–Markov model is employed as the prior PDF. Experimental results are presented to illustrate the effectiveness of the proposed method.