Browsing by Subject "Remote sensing--Data processing."

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Open Access
Hierarchical segmentation, object detection and classification in remotely sensed images
(2007) Akçay, Hüseyin Gökhan
Automatic content extraction and classification of remotely sensed images have become highly desired goals by the advances in satellite technology and computing power. The usual choice for the level of processing image data has been pixelbased analysis. However, spatial information is an important element to interpret the land cover because pixels alone do not give much information about image content. Automatic segmentation of high-resolution remote sensing imagery is an important problem in remote sensing applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. In this thesis, we first present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. First, principal components are computed from hyper-spectral data to obtain representative bands. Then, candidate regions are extracted by applying connected components analysis to the pixels selected according to their morphological profiles computed using opening and closing by reconstruction with increasing structuring element sizes. Next, these regions are represented using a tree, and the most meaningful ones are selected by optimizing a measure that consists of two factors: spectral homogeneity, which is calculated in terms of variances of spectral features, and neighborhood connectivity, which is calculated using sizes of connected components. Experiments on three data sets show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information.Then, we introduce an unsupervised method that combines both spectral and structural information for automatic object detection. First, a segmentation hierarchy is constructed and candidate segments for object detection are selected by the proposed segmentation method. Given the observation that different structures appear more clearly in different principal components, we present an algorithm that is based on probabilistic Latent Semantic Analysis (PLSA) for grouping the candidate segments belonging to multiple segmentations and multiple principal components. The segments are modeled using their spectral content and the PLSA algorithm builds object models by learning the objectconditional probability distributions. Labeling of a segment is done by computing the similarity of its spectral distribution to the distribution of object models using Kullback-Leibler divergence. Experiments on three data sets show that our method is able to automatically detect, group, and label segments belonging to the same object classes. Finally, we present an approach for classification of remotely sensed imagery using spatial information extracted from multi-scale segmentations. Different structuring element size ranges are used to obtain multiple representations of an image at different scales to capture different details inherently found in different structures. Then, pixels at each scale are grouped into contiguous regions using the proposed segmentation method. The resulting regions are modeled using the statistical summaries of their spectral properties. These models are used to cluster the regions by the proposed grouping method, and the cluster memberships assigned to each region at multiple scales are used to classify the corresponding pixels into land cover/land use categories. Final classification is done using decision tree classifiers. Experiments with three ground truth data sets show the effectiveness of the proposed approach over traditional techniques that do not make strong use of region-based spatial information.
Open Access
Structural scene analysis of remotely sensed images using graph mining
(2010) Özdemir, Bahadır
The need for intelligent systems capable of automatic content extraction and classi cation in remote sensing image datasets, has been constantly increasing due to the advances in the satellite technology and the availability of detailed images with a wide coverage of the Earth. Increasing details in very high spatial resolution images obtained from new generation sensors have enabled new applications but also introduced new challenges for object recognition. Contextual information about the image structures has the potential of improving individual object detection. Therefore, identifying the image regions which are intrinsically heterogeneous is an alternative way for high-level understanding of the image content. These regions, also known as compound structures, are comprised of primitive objects of many diverse types. Popular representations such as the bag-of-words model use primitive object parts extracted using local operators but cannot capture their structure because of the lack of spatial information. Hence, the detection of compound structures necessitates new image representations that involve joint modeling of spectral, spatial and structural information. We propose an image representation that combines the representational power of graphs with the e ciency of the bag-of-words representation. The proposed method has three parts. In the rst part, every image in the dataset is transformed into a graph structure using the local image features and their spatial relationships. The transformation method rst detects the local patches of interest using maximally stable extremal regions obtained by gray level thresholding. Next, these patches are quantized to form a codebook of local information and a graph is constructed for each image by representing the patches as the graph nodes and connecting them with edges obtained using Voronoi tessellations. Transforming images to graphs provides an abstraction level and the remaining operations for the classi cation are made on graphs. The second part of the proposed method is a graph mining algorithm which nds a set of most important subgraphs for the classi cation of image graphs. The graph mining algorithm we propose rst nds the frequent subgraphs for each class, then selects the most discriminative ones by quantifying the correlations between the subgraphs and the classes in terms of the within-class occurrence distributions of the subgraphs; and nally reduces the set size by selecting the most representative ones by considering the redundancy between the subgraphs. After mining the set of subgraphs, each image graph is represented by a histogram vector of this set where each component in the histogram stores the number of occurrences of a particular subgraph in the image. The subgraph histogram representation enables classifying the image graphs using statistical classi ers. The last part of the method involves model learning from labeled data. We use support vector machines (SVM) for classifying images into semantic scene types. In addition, the themes distributed among the images are discovered using the latent Dirichlet allocation (LDA) model trained on the same data. By this way, the images which have heterogeneous content from di erent scene types can be represented in terms of a theme distribution vector. This representation enables further classi cation of images by theme analysis. The experiments using an Ikonos image of Antalya show the e ectiveness of the proposed representation in classi cation of complex scene types. The SVM model achieved a promising classi cation accuracy on the images cut from the Antalya image for the eight high-level semantic classes. Furthermore, the LDA model discovered interesting themes in the whole satellite image.