Jiaojiao Tian

Building Change Detection Based on Satellite Stereo Imagery and Digital Surface Models

Building change detection is a major issue for urban area monitoring. Due to different imaging conditions and sensor parameters, 2-D information delivered by satellite images from different dates is often not sufficient when dealing with building changes. Moreover, due to the similar spectral characteristics, it is often difficult to distinguish buildings from other man-made constructions, like roads and bridges, during the change detection procedure. Therefore, stereo imagery is of importance to provide the height component which is very helpful in analyzing 3-D building changes. In this paper, we propose a change detection method based on stereo imagery and digital surface models (DSMs) generated with stereo matching methodology and provide a solution by the joint use of height changes and Kullback-Leibler divergence similarity measure between the original images. The Dempster-Shafer fusion theory is adopted to combine these two change indicators to improve the accuracy. In addition, vegetation and shadow classifications are used as no-building change indicators for refining the change detection results. In the end, an object-based building extraction method based on shape features is performed. For evaluation purpose, the proposed method is applied in two test areas, one is in an industrial area in Korea with stereo imagery from the same sensor and the other represents a dense urban area in Germany using stereo imagery from different sensors with different resolutions. Our experimental results confirm the efficiency and high accuracy of the proposed methodology even for different kinds and combinations of stereo images and consequently different DSM qualities.

Spatiotemporal inferences for use in building detection using series of very-high-resolution space-borne stereo images

Automatic building detection from very-high-resolution (VHR) satellite images is a difficult task. The detection accuracy is usually limited by spectral ambiguities and the uncertainties of the available height information. Feature extraction and training sampling collection for supervised methods are other sources of uncertainty. Most widely used VHR sensors have shorter revisit cycles (IKONOS/GeoEye-1/2, 3 days; WorldView 1/2, 1.1 days) due to large off-nadir viewing angles and hence are able to perform consistent acquisition of mono or stereo images. In this article, we investigate the possibility of using high-temporal stereo VHR images to enhance remote-sensing image interpretation under the context of building detection. Digital surface models, which contain the height information, are generated for each date using semi-global matching. Pre-classification is performed combining the height and spectral information to obtain an initial building probability map. With a reference land cover map available for one date, the training samples of the other dates are automatically derived using a rule-based validating procedure. A spatiotemporal inference filter is developed considering the spectral, spatial, and temporal aspects to enhance the building probability maps. This aims at homogenizing the building probability values of spectrally similar pixels in the spatial domain and geometrically similar pixels in the temporal domain, while being robust to the silhouette of the images and geometric discrepancies of the multitemporal data. The effectiveness and robustness of the proposed method are evaluated by performing three experiments on six stereo pairs of the same region over a time period of five years (2006–2011). The area under curve (AUC) of the receiver operating characteristic and kappa statistic (κ) are employed to assess the results. These experiments show that spatiotemporal inference filtering largely improves the accuracy of the building probability map (average AUC = 0.95) while facilitating building extraction in snow-covered images. The resulting building probability maps can be further used for other applications (e.g. building footprint updating).

Cultural Heritage Sites in Danger—Towards Automatic Damage Detection from Space

The intentional damage to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess this damage in such non-accessible areas, and automated image processing techniques will be needed to speed up the analysis if a fast response is desired. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas, based on the extraction of textural information and robust differences of brightness values related to pre- and post-disaster satellite images. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites of interest. Encouraging results are obtained for two archaeological sites in Syria and Iraq.

Improving Change Detection in Forest Areas Based on Stereo Panchromatic Imagery Using Kernel MNF

The goal of this paper is to develop an efficient method for forest change detection using multitemporal stereo panchromatic imagery. Due to the lack of spectral information, it is difficult to extract reliable features for forest change monitoring. Moreover, the forest changes often occur together with other unrelated phenomena, e.g., seasonal changes of land covers such as grass and crops. Therefore, we propose an approach that exploits kernel Minimum Noise Fraction (kMNF) to transform simple change features into high-dimensional feature space. Digital surface models (DSMs) generated from stereo imagery are used to provide information on height difference, which is additionally used to separate forest changes from other land-cover changes. With very few training samples, a change mask is generated with iterated canonical discriminant analysis (ICDA). Two examples are presented to illustrate the approach and demonstrate its efficiency. It is shown that with the same amount of training samples, the proposed method can obtain more accurate change masks compared with algorithms based on k-means, one-class support vector machine, and random forests.

Fusion of multi-spectral bands and DSM from WorldView-2 stereo imagery for building extraction

This paper describes a building extraction approach by fusion panchromatic image, multispectral image and Digital Surface Model (DSM) generated from WorldView-2 stereo imagery. First, the DSM is used to indicate a rough building location, and then Hough-transform is followed to generate 2D segments from panchromatic images. Hough lines in the two main directions of each building are kept. The probability of each segment belonging to a building is calculated from random forest estimations. An automatic training data selection strategy is designed for this supervised classification. Finally, the 2D segments and classification results are combined to get the final building boundaries. This method has been tested in the city center of Munich, Germany.

Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection

Digital surface models (DSMs) derived from spaceborne and airborne sensors enable the monitoring of the vertical structures for forests in large areas. Nevertheless, due to the lack of an objective performance assessment for this task, it is difficult to select the most appropriate data source for DSM generation. In order to fill this gap, this paper performs change detection analysis including forest decrease and tree growth. The accuracy of the DSMs is evaluated by comparison with measured tree heights from inventory plots (field data). In addition, the DSMs are compared with LiDAR data to perform a pixel-wise quality assessment. DSMs from four different satellite stereo sensors (ALOS/PRISM, Cartosat-1, RapidEye and WorldView-2), one satellite InSAR sensor (TanDEM-X), two aerial stereo camera systems (HRSC and UltraCam) and two airborne laser scanning datasets with different point densities are adopted for the comparison. The case study is a complex central European temperate forest close to Traunstein in Bavaria, Germany. As a major experimental result, the quality of the DSM is found to be robust to variations in image resolution, especially when the forest density is high. The forest decrease results confirm that besides aerial photogrammetry data, very high resolution satellite data, such as WorldView-2, can deliver results with comparable quality as the ones derived from LiDAR, followed by TanDEM-X and Cartosat DSMs. The quality of the DSMs derived from ALOS and Rapid-Eye data is lower, but the main changes are still correctly highlighted. Moreover, the vertical tree growth and their relationship with tree height are analyzed. The major tree height in the study site is between 15 and 30 m and the periodic annual increments (PAIs) are in the range of 0.30–0.50 m.

Cloud Removal in Image Time Series through Sparse Reconstruction from Random Measurements

In this paper, we propose a cloud removal algorithm for scenes within a satellite image time series based on synthetization of the affected areas via sparse reconstruction. The high spectrotemporal dimensionality of time series allows applying pixel-based sparse reconstruction techniques efficiently, estimating the values below a cloudy area by observing the spectral evolution in time of pixels in cloud-free areas. The process implicitly compensates the overall atmospheric interactions affecting a given image, and it is possible even if only one acquisition is available for a given period of time. The dictionary, on the basis of which the data are reconstructed, is selected randomly from the available image elements in the time series. This increases the degree of automation of the process, if the area containing clouds and their shadows is given. Favorable comparisons with similar methods and applications to supervised classification and change detection show that the proposed algorithm restores images locally contaminated by clouds and their shadows in a satisfactory and efficient way.

Building change detection in satellite stereo imagery based on belief functions

3D Building change detection has become a popular research topic along with the improvement of image quality and computer science. When only building changes are of interest, both the multi-temporal images and Digital Surface Models provide valuable but not comprehensive information in the change detection procedure. Therefore, in this paper, belief functions have been adopted for fusing information from these two sources. In the first step, two change indicators are proposed by focusing on building changes. Both indicators have been projected to a sigmoid curve, in which both the concordance and discordance indexes are considered. In order to fuse the concordance and discordance indexes and further fuse the two change indicators, two belief functions are considered. One is the original Dempster-Shafer Theory (DST), and the most recent one is Dezert-Smarandache Theory (DSmT). This paper shows how these belief-based frameworks can help in building change detection problem. Besides using different belief functions in obtaining the global BBAs, four decision-making criteria are tested to extract final building change masks. The results have been validated by compared to the manually extracted change reference mask.

Building damage assessment after the earthquake in Haiti using two post-event satellite stereo imagery and DSMs

In this paper, a novel disaster building damage monitoring method is presented. This method combines the multispectral imagery and DSMs from stereo matching to obtain three kinds of changes. The proposed method contains three basic steps. The first step is to segment the panchromatic images to get the smallest possible homogeneous regions. In the second step, based on a rule based classification using change information from Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) and height, the changes are classified to ruined buildings, new buildings, and changes without height change (mainly temporary residential area, etc. tents). In the last step, a region based grey level co-occurrence matrix texture measurement is used to refine the third change class. The method is applied to building change detection after the Haiti earthquake.

Fusion of multispectral imagery and DSMs for building change detection using belief functions and reliabilities

ABSTRACT The extraction of building changes from very high resolution satellite images is an important but challenging task in remote sensing. Digital Surface Models (DSMs) generated from stereo imagery have proved to be valuable additional data sources for this task. In order to efficiently use the change information from the DSMs and spectral images, belief functions have .been introduced. In this article, two-step building change detection fusion models based on both Dempster-Shafer Theory (DST) and Dezert-Smarandache Theory (DSmT) frameworks are proposed. In the first step, basic belief assignments (BBAs) of the change indicators from images and DSMs are calculated by using a refined sigmoidal BBA model. Then these BBAs are employed for the new proposed building change detection decision fusion approach. In order to cover the miss-detections introduced by the wrong height values of the DSMs and incomplete information from images, disparity maps from the DSM generation procedure and shadow maps from the multispectral channels are adopted to generate reliability maps, which are further integrated to the fusion models. In the last step, building change masks are generated based on four decision-making criteria. In the experimental part of this work, we evaluate the performance of this new building change detection method on real satellite images thanks to a building change reference mask representing the ground truth. Substantial accuracy improvements are achieved when comparing the new results with those obtained from classical 3D change detection approaches.