Rapidly and accurately detecting individual tree crowns in satellite imagery is a critical need for monitoring and characterizing forest resources. We present a two-stage semiautomated approach for detecting individual tree crowns using high spatial resolution (0.6 m) satellite imagery. First, active contours are used to recognize tree canopy areas in a normalized difference vegetation index image. Given the image areas corresponding to tree canopies, we then identify individual tree crowns as local extrema points in the Laplacian of Gaussian scale-space pyramid. The approach simultaneously detects tree crown centers and estimates tree crown sizes, parameters critical to multiple ecosystem models. As a demonstration, we used a ground validated, 0.6 m resolution QuickBird image of a sparse forest site. The two-stage approach produced a tree count estimate with an accuracy of 78% for a naturally regenerating forest with irregularly spaced trees, a success rate equivalent to or better than existing approaches. In addition, our approach detects tree canopy areas and individual tree crowns in an unsupervised manner and helps identify overlapping crowns. The method also demonstrates significant potential for further improvement.
We present a multi-scale framework for man-made structures cuing in satellite image regions. The approach is based on
a hierarchical image segmentation followed by structural analysis. A hierarchical segmentation produces an image
pyramid that contains a stack of irregular image partitions, represented as polygonized pixel patches, of successively
reduced levels of detail (LODs). We are jumping off from the over-segmented image represented by polygons attributed
with spectral and texture information. The image is represented as a proximity graph with vertices corresponding to the
polygons and edges reflecting polygon relations. This is followed by the iterative graph contraction based on Boruvka's
Minimum Spanning Tree (MST) construction algorithm. The graph contractions merge the patches based on their
pairwise spectral and texture differences. Concurrently with the construction of the irregular image pyramid, structural
analysis is done on the agglomerated patches. Man-made object cuing is based on the analysis of shape properties of the
constructed patches and their spatial relations. The presented framework can be used as pre-scanning tool for wide area
monitoring to quickly guide the further analysis to regions of interest.
Reliable and accurate methods for detection and extraction of linear network, such as road networks, in satellite imagery are essential to many applications. We present an approach to the road network extraction from high-resolution satellite imagery that is based on proximity graph analysis. We are jumping off from the classification provided by existing spectral and textural classification tools, which produce a set of candidate road patches. Then, constrained Delaunay triangulation and Chordal Axis transform are used to extract centerline characterization of the delineated candidate road patches. We refine produced center lines to reduce noise influence on patch boundaries, resulting in a smaller set of robust center lines authentically representing their road patches. Refined center lines are triangulated using constrained Delaunay triangulation (CDT) algorithm to generate a sub-optimal mesh of interconnections among them. The generated triangle edges connecting different center lines are used for spatial analysis of the center lines relations. A subset of the Delaunay tessellation grid contains the Euclidian Minimum Spanning Tree (EMST) that provides an approximation of road network. The approach can be generalized to the multi-criteria MST and multi-criteria shortest path algorithms to integrate other factors important for road network extraction, in addition to proximity relations considered by standard EMST.
The representation and characterization of planar shapes or regions has important consequences for image processing and image content understanding. Numerous approaches have been developed to provide image analysis with efficient methods to represent shapes. We present a region-based approach to extract a refined axial regional shape description in the form of a skeleton, which is based on the use of the constrained Delaunay triangulation (CDT) and the chordal axis transform (CAT). We elaborate on the exploitation of the approximate edge co-circularity criterion that is used to refine CAT-produced skeletons. The co-circularity criterion enables efficient evaluation of CDT-generated triangle edges lying inside regions (chords), and filters out non-salient chords. The application of this criterion produces smoother skeletons, allows skeleton to have vertices with branching degree higher than three that was due to the use of CDT, and significantly reduces number of skeleton segments. In this paper, in contrast with the chord strength evaluation of the original skeleton rectification algorithm, where chord strength evaluation does not include strengths of its neighboring chords, we introduce smoothing operator to evaluate chord strength by processing chord strengths within local neighborhood. The result of region characterization based on the proposed smoothing-based chord evaluation is that skeleton is more authentic (sensitive) to original shape, while at the same time it preserves all the advantages of the original skeleton rectification scheme. A number of examples, including comparison with skeletons generated by the original CAT-skeleton rectification algorithm, are presented to demonstrate our approach at work.
Reliable and accurate methods for road network detection and classification in satellite imagery are essential to many applications. We present an image vectorization approach to the road network extraction from digital imagery that is based on proximity graph analysis. An input to the presented approach is spectrally segmented image that contains a set of candidate road fragments. First, non-intersecting contours are extracted around image elements. Second, constrained Delaunay triangulation and Chordal Axis transform are used to extract global centerline topology characterization of the delineated candidate road fragments. Then, constrained Delaunay triangulation of the extracted set of attributed center lines is performed. The tessellation grid of the Delaunay triangulation covers the set of candidate road fragments and is adapted to its structure, since triangle vertices and edges reflect the shapes and spatial adjacency of the segmented regions. The produced Delaunay network edges can be attributed with spectral and structural characteristics that are used for spatial analysis of the edges relations. This leads to the reconstruction of the road network out of the Delaunay edges. A subset of the tessellation grid contains the Euclidian Minimum Spanning Tree that provides an approximation of road network. The approach can be generalized to the multi-criteria MST and multi-criteria shortest path algorithms to integrate other factors important for road network extraction, in addition to proximity relations considered by standard MST.
Object boundary extraction from binary images is important for many applications, e.g., image vectorization, automatic interpretation of images containing segmentation results, printed and handwritten documents and drawings, maps, and AutoCAD drawings. Efficient and reliable contour extraction is also important for pattern recognition due to its impact on shape-based object characterization and recognition. The presented contour tracing and component labeling algorithm produces dilated (sub-pixel) contours associated with corresponding regions. The algorithm has the following features: (1) it always produces non-intersecting, non-degenerate contours, including the case of one-pixel wide objects; (2) it associates the outer and inner (i.e., around hole) contours with the corresponding regions during the process of contour tracing in a single pass over the image; (3) it maintains desired connectivity of object regions as specified by 8-neighbor or 4-neighbor connectivity of adjacent pixels; (4) it avoids degenerate regions in both background and foreground; (5) it allows an easy augmentation that will provide information about the containment relations among regions; (6) it has a time complexity that is dominantly linear in the number of contour points. This early component labeling (contour-region association) enables subsequent efficient object-based processing of the image information.
We demonstrate how to derive morphological information from micrographs, i.e., grey-level images, of polymeric foams. The segmentation of the images is performed by applying a pulse-coupled neural network. This processing generates blobs of the foams walls/struts and voids, respectively. The contours of the blobs and their corresponding points form the input to a constrained Delaunay tessellation, which provides an unstructured grid of the material under consideration. The subsequently applied Chordal Axis Transform captures the intrinsic shape characteristics, and facilitates the identification and localization of key morphological features. While stochastic features of the polymeric foams struts/walls such as areas, aspect ratios, etc., already can be computed at this stage, the foams voids require further geometric processing. The voids are separated into single foam cells. This shape manipulation leads to a refinement of the initial blob contours, which then requires the repeated application of the constrained Delaunay tessellation and Chordal Axis Transform, respectively. Using minimum enclosing rectangles for each foam cell, finally the stochastic features of the foam voids are computed.
We present a new method to transform the spectral pixel information of a micrograph into an affine geometric description, which allows us to analyze the morphology of granular materials. We use spectral and pulse-coupled neural network based segmentation techniques to generate blobs, and a newly developed algorithm to extract dilated contours. A constrained Delaunay tessellation of the contour points results in a triangular mesh. This mesh is the basic ingredient of the Chodal Axis Transform, which provides a morphological decomposition of shapes. Such decomposition allows for grain separation and the efficient computation of the statistical features of granular materials.
We present a syntactic and metric two-dimensional shape recognition scheme based on shape features. The principal features of a shape can be extracted and semantically labeled by means of the chordal axis transform (CAT), with the resulting generic features, namely torsos and limbs, forming the primitive segmented features of the shape. We introduce a context-free universal language for representing all connected planar shapes in terms of their external features, based on a finite alphabet of generic shape feature primitives. Shape exteriors are then syntactically represented as strings in this language. Although this representation of shapes is not complete, in that it only describes their external features, it effectively captures shape embeddings, which are important properties of shapes for purposes of recognition. The elements of the syntactic strings are associated with attribute feature vectors that capture the metrical attributes of the corresponding features. We outline a hierarchical shape recognition scheme, wherein the syntactical representation of shapes may be 'telescoped' to yield a coarser or finer description for hierarchical comparison and matching. We finally extend the syntactic representation and recognition to completely represent all planar shapes, albeit without a generative context-free grammar for this extension.
Image analysis is an important requirement of many artificial intelligence systems. Though great effort has been devoted to inventing efficient algorithms for image analysis, there is still much work to be done. It is natural to turn to mammalian vision systems for guidance because they are the best known performers of visual tasks. The pulse- coupled neural network (PCNN) model of the cat visual cortex has proven to have interesting properties for image processing. This article describes the PCNN application to the processing of images of heterogeneous materials; specifically PCNN is applied to image denoising and image segmentation. Our results show that PCNNs do well at segmentation if we perform image smoothing prior to segmentation. We use PCNN for obth smoothing and segmentation. Combining smoothing and segmentation enable us to eliminate PCNN sensitivity to the setting of the various PCNN parameters whose optimal selection can be difficult and can vary even for the same problem. This approach makes image processing based on PCNN more automatic in our application and also results in better segmentation.
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