Rukban is a desert area crossing the border between Syria and Jordan, and thousands of Syrian refugees fled into this area since the Syrian civil war in 2014. In the past few years, the number of refugee shelters for the forcibly displaced Syrian refugees in this area has increased rapidly. Estimating the location and number of refugee tents has become a key factor to maintain the sustainability of the refugee shelter camps. Manually counting the shelters is labor-intensive and sometimes prohibitive given the large quantities. In addition, these shelters/tents are usually small in size, irregular in shape, and sparsely distributed in a very large area and could be easily missed by the traditional image-analysis techniques, making the image-based approaches also challenging. In this letter, we proposed a deep fully convolutional neural network (FCN) model to extract automatically the refugee shelters/tents in the worldview-2 (WV-2) satellite images. In addition, we transferred knowledge in the pretrained VGG-16 model to improve the detection accuracy and network training convergence. We compared the proposed approach with the traditional spectral angle mapper (SAM) method, deep convolutional neural network (CNN) models, and the mask Region-based CNN (R-CNN) model. The experimental results show that the FCN model improved the overall accuracy by 4.49%, 3.54%, and 0.88% compared with the CNNs, SAM, and mask R-CNN models, and improved the precision by 34.61%, 41.99%, and 11.87%, respectively.

Soil can be used as a damage indicator of landslides and flooding, which expose soil from vegetation canopy. It can also be used as an indirect indicator of illegal tunnel digging activity. This letter presents a sparsity-based approach to soil detection using multispectral satellite images, where both original and synthetic bands have been used. Spatial and spectral information has then been jointly used in soil detection. Extensive experiments clearly demonstrated the feasibility of our approach.

A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.

Date of Conference: August 22 - 25, 2004

The VisiMine project aims to provide infrastructure that would enable the analysis of large databases containing satellite images. Our work addresses two issues. One is the extraction of information that enables reduction of the data from multi-spectral images into a number of features. Second is the organization and selection of the features that would allow flexible and scalable discovery of the knowledge from the databases of remotely sensed images. The VisiMine architecture distinguishes between three types of feature vectors: pixel, region and tile. One of the challenges in information retrieval is the proper choice of the set of features that are the best suited for a data mining task. The VisiMine system enables extraction of a large number of features that describe textural and spectral properties of satellite information, in addition to the analysis of image information, the system can perform data fusion of image properties with auxiliary data such as DEM. Tilton et al. (2002) presented the results of the information retrieval experiments with the Hierarchical Segmentation (HSEG) algorithm that produces a hierarchical set of image segmentations. The results presented showed that the use of HSEG features improves the precision and recall of similarity searches. However, for different types of land cover, different combinations of HSEG segmentation levels and textural features provided the best results. Image analysis applications often require different levels of image segmentation detail as well as the use of different mixes of spectral, textural and shape features combined together with auxiliary information. Furthermore, a particular application may require different features and different levels of image segmentation detail depending on how the image objects are being analyzed. Thus, an automatic selection of feature sets would be very useful for satellite image analysis. In this paper, we present algorithms that allow for automatic selection of features for region and tile similarity searches. The relevance feedback technique allows for selective choices to be made in the region(s) of interest for which a good subset of features may be found in real time. The preliminary results of the experiments with LANDSAT data show improvements in both precision and recall over previously used methods.

We describe a system for content-based retrieval and classification of multispectral images. Our system models images on pixel, region and scene levels. To reduce the gap between low-level features and highlevel user semantics, and to support complex query scenarios that consist of many regions with different feature characteristics, we propose a probabilistic visual grammar that includes automatic identification of region prototypes and modeling of their spatial relationships. A Bayesian framework is used to automatically classify scenes based on these models. We demonstrate our system with query scenarios that cannot be expressed by traditional region or scene level approaches but where the visual grammar provides accurate classifications and effective retrieval.

We describe usage of DEM data in the VisiMine system for data mining and statistical analysis of the collections of remotely sensed images.

We describe VisiMine, a system for data mining and statistical analysis of large collections of remotely sensed images.

The hierarchical segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integrating nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for the computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies being proposed, including approximation and selective materialization of the spatial objects resulting from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called object-based selective materialization, which is different from cuboid-based selective materialization (proposed in previous studies of nonspatial data cube construction). Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level: that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes, and a performance study has demonstrated the effectiveness of these techniques.

Spatial data mining is to mine high-level spatial information and knowledge from large spatial databases. A spatial data mining system prototype, GeoMiner, has been designed and developed based on our years of experience in the research and development of relational data mining system, DBMiner, and our research into spatial data mining. The data mining power of GeoMiner includes mining three kinds of rules: characteristic rules, comparison rules, and association rules, in geo-spatial databases, with a planned extension to include mining classification rules and clustering rules. The SAND (Spatial And Nonspatial Data) architecture is applied in the modeling of spatial databases, whereas GeoMiner includes the spatial data cube construction module, spatial on-line analytical processing (OLAP) module, and spatial data mining modules. A spatial data mining language, GMQL (Geo-Mining Query Language), is designed and implemented as an extension to Spatial SQL [3], for spatial data mining. Moreover, an interactive, user-friendly data mining interface is constructed and tools are implemented for visualization of discovered spatial knowledge.

Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules, and meta-rule guided knowledge mining, the system provides a user-friendly, interactive data mining environment with good performance.