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Salt body extraction plays an important role in the analysis of salt structures and the exploration of oil and gas. Seismic attributes and edge detection algorithms, which require manual effort, are the conventional methods of extracting salt boundaries from seismic images. Convolutional neural networks (CNNs) have become the state-of-the-art automatic segmentation method for seismic interpretation. However, the fully automatic results of the extraction of salt boundaries may still need to be modified to become accurate and robust enough for practical production. We present a novel deep-learning-based interactive segmentation method for extracting salt boundaries. To incorporate the interaction points into our method, we transform positive and negative points into two Euclidean distance maps (EDMs), which are combined with seismic images to train our CNN model. The model is composed of a U-net and a pyramid pooling module (PPM), and it is trained on the Tomlinson Geophysical Services (TGS) Salt Identification Challenge dataset. Then, we use a graph cut algorithm to refine the likelihood maps predicted by our CNN model and, subsequently, update the salt boundaries. Some field examples show that the proposed method outperforms fully automatic CNN methods with a higher matching degree of the ground truth. [ABSTRACT FROM AUTHOR]

This paper presents a novel Markov random field (MRF) method integrating adaptive interclass-pair penalty (aICP2) and spectral similarity information (SSI) for hyper-spectral image (HSI) classification. aICP2 structurally combines $K(K - 1)/2$ (K is the number of classes) classical “Potts model” with $K(K - 1)/2$ interaction coefficients. aICP2 tries a new way to solve the key problems, insufficient correction within homogeneous regions, and over-smoothness at class boundaries, in MRF-based HSI classification. It is assumed that different class pairs should be assigned with various degrees of penalties in MRF smoothness process, according to pairwise class separability and spatial class confusion in raw classification map. The Fisher ratio is modified to measure pairwise class separability with a training set. And, gray level co-occurrence matrix is used to measure spatial class confusion degree. Then, aICP2 is constructed by combining Fisher ratio and GCLM. aICP2 applies larger penalty on class pairs that confuse with each other seriously to provide sufficient smoothness, and vice versa. In addition, to protect class edges and details, SSI is introduced to make the penalty of related neighboring pixels small. aICP2ssi denotes the integration of aICP2 and SSI. The further improved method is both interclass-pair and interpixel adaptive in spatial term. A graph-cut-based $\alpha - \beta $ -swap method is introduced to optimize the proposed energy function. The experimental results on real HSI data indicate that the proposed method outperforms compared MRF-based and other spectral–spatial approaches in terms of classification accuracies and region uniformity. [ABSTRACT FROM AUTHOR]

With the fast advancement of remote sensing platforms and sensors, remotely sensed imagery (RSI) is increasingly being characterized by both high spatial resolution and high temporal resolution. How to efficiently use the rich spatial and temporal information in RSI for highly accurate object detection and classification is an important research question. Nevertheless, there is still a lack of a probabilistic framework that is capable of fully accounting for the spatial–temporal information in RSI for improved applications. In this paper, we present a Bayesian spatial–temporal random field model that constitutes a complete probabilistic framework for fully explaining the spatial–temporal correlation in RSI, leading to an enhanced object detection approach that is used for cloud detection from RSI. Under the Bayesian theorem, the posterior distribution of a label field is decomposed into the label prior, the data likelihood, the temporal label likelihood, and the temporal data likelihood. To address the difficulties in modeling the complex spatial–temporal correlation effect in the temporal data likelihood, a stochastic sampling approach is presented. Based on the maximum a posteriori approach, the posterior distribution is seamlessly integrated into the graph-cut optimization framework, and, therefore, the model optimization can be efficiently solved. The proposed algorithm is tested for cloud detection on both simulated and real RSIs and the results demonstrate that the proposed algorithm can effectively exploit the spatial–temporal information for achieving higher detection accuracy. [ABSTRACT FROM PUBLISHER]

This paper presents a cosegmentation-based method for building change detection from multitemporal high-resolution (HR) remotely sensed images, providing a new solution to object-based change detection (OBCD). First, the magnitude of a difference image is calculated to represent the change feature. Next, cosegmentation is performed via graph-based energy minimization by combining the change feature with image features at each phase, directly resulting in foreground as multitemporal changed objects and background as unchanged area. Finally, the spatial correspondence between changed objects is established through overlay analysis. Cosegmentation provides a separate and associated, rather than a separate and independent, multitemporal image segmentation method for OBCD, which has two advantages: 1) both the image and change features are used to produce foreground segments as changed objects, which can take full advantage of multitemporal information and produce two spatially corresponded change detection maps by the association of the change feature, having the ability to reveal the thematic, geometric, and numeric changes of objects and 2) the background in the cosegmentation result represents the unchanged area, which naturally avoids the problem of matching inconsistent unchanged objects caused by the separate and independent multitemporal segmentation strategy. Experimental results on five HR datasets verify the effectiveness of the proposed method and the comparisons with the state-of-the-art OBCD methods further show its superiority. [ABSTRACT FROM AUTHOR]

This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed. [ABSTRACT FROM AUTHOR]

Very high resolution satellite images provide valuable information to researchers. Among these, urban-area boundaries and building locations play crucial roles. For a human expert, manually extracting this valuable information is tedious. One possible solution to extract this information is using automated techniques. Unfortunately, the solution is not straightforward if standard image processing and pattern recognition techniques are used. Therefore, to detect the urban area and buildings in satellite images, we propose the use of scale invariant feature transform (SIFT) and graph theoretical tools. SIFT keypoints are powerful in detecting objects under various imaging conditions. However, SIFT is not sufficient for detecting urban areas and buildings alone. Therefore, we formalize the problem in terms of graph theory. In forming the graph, we represent each keypoint as a vertex of the graph. The unary and binary relationships between these vertices (such as spatial distance and intensity values) lead to the edges of the graph. Based on this formalism, we extract the urban area using a novel multiple subgraph matching method. Then, we extract separate buildings in the urban area using a novel graph cut method. We form a diverse and representative test set using panchromatic 1-m-resolution Ikonos imagery. By extensive testings, we report very promising results on automatically detecting urban areas and buildings. [ABSTRACT FROM AUTHOR]