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This paper develops a novel oil spill detection approach by using the multitemporal optical remote sensing images. Differently from the traditional oil spill detection methods that mainly carried out on a monotemporal image, the proposed approach opens a new perspective to solve the considered oil spill detection problem in a multitemporal domain by investigating the potential capability of change...
Continuous anomaly detection in satellite image time series is important for studying spatial-temporal processes of land cover changes. In another study, we proposed a method based on Z-scores of Season-Trend model Residuals (Z-STR), which can continuously detects anomaly regions in image time series. However, Z-STR only detects anomalies with large shifts but cannot detect anomalies with small or...
Recently, a thresholding method based on the Rayleigh-Rice mixture has been proposed for solving binary change detection problems in multispectral image pairs. However, when images acquired by the last generation of multispectral scanners having high radiometric resolution are considered, the distribution fitting is still not satisfactory and computed thresholds remain quite distant from the optimal...
In this paper, we propose a Spark-based fuzzy local information C-Means (FLICM) algorithm that provides synthetic aperture radar (SAR) image change detection. With the volume and resolution of SAR images increasing, current serial clustering algorithms are not suitable to handle big data, scalable solutions are indispensable. The proposed algorithm based on Spark framework implements FLICM algorithm...
Remote sensing technology plays an important role in gathering information of social infrastructure damage which is crucial for relief and reconstruction work after strike. Comparatively speaking, radar sensors are capable of observing the ground irrespective of weather conditions or the time of the day, and therefore have been gaining prominence as a reliable tool for grasping the overall picture...
Image change detection has a wide range of applications in various fields, such as damage assessment, environmental monitoring and agricultural surveys. As the number of remote sensing images and the complexity of algorithm rise, the demand for processing power is increasing. In this paper, we present a parallel FLICM algorithm for SAR image change detection on Intel MIC (Many Integrated Core) which...
The Weihai area in Shandong province has undergone intensive changes of the landscape over the past years. The city has expanded to a high percentage but is still characterized by a huge number of lakes and surrounding coastal waters (case-II). In the study, we investigated the temperature changes of those lakes and the coastal zones as well as of some features at land over the past 20 years. We were...
The parcel-based changed detection by adopting the holistic feature can extract the changed parcels in land-use maps[1]. This method of parcel-based change detection uses the holistic feature, which represents each land use parcels clipped by polygons in the land use map with the energy spectrum of WFT and extracts the changed parcels according to the distance threshold between feature vectors associated...
Documenting liquefaction surface effects is necessary both to validate and to refine existing liquefaction models. Post-earthquake liquefaction data collection historically relies on field investigation and is often spatially limited and incomplete [4]. Areas that are not near centers of population or major transportation routes are often neglected. Pre- and post-earthquake satellite images enable...
Intensive transformation of landscape has taken place in Weihai, Shandong province. This gave us the idea to investigate the urban development in this area by the use of archived satellite data, available for the last 30 years. We utilized advanced processing schemes to geometrically correct and spectrally calibrate about 60 cloud free frames of Landsat data available from 1984 to 2015. We used different...
Greater Montreal is the most populous metropolitan area in Quebec, and the second most populous in Canada after Greater Toronto. In the 1970s, the economic center of Canada shifted from Montreal to Toronto. Since some previous studies have focused on the urbanization process in the Greater Toronto Area, it is important to conduct research on its counterpart. This study uses Landsat images as the data...
An automatic change detection method based on conditional random field (CRF) is presented for high resolution remote sensing images in this paper. Marginalized denoising autoencoder is used to generate the difference image. The clustering results of Fuzzy C-means are applied to initialize the unary potentials of CRF. A scaled squared Euclidean distance between neighboring pixels in the observed images...
Regional monitoring of land cover conversion of natural vegetation to new informal human settlements is essential when investigating the migration of people to urbanized cities. Detecting these new settlements require reliable change detection methods. A robust change detection metric can be derived by analyzing the area under the autocorrelation function for a time series. The time dependence on...
One of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the information from the different modalities. In this paper, we propose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important properties and...
Change Vector Analysis (CVA) is an important change detection method for remote sensing imagery with medium and low resolution. Traditional CVA is a pixel-based method, which is insufficient for high-resolution (HR) imagery. An object-oriented change vector analysis method (OCVA) is proposed in the paper. Image segmentation method was used to get image objects. The object histogtam was extracted as...
Natural disasters or human activities, such as forest fire, flood, and deforestation, may lead to anomaly or disturbance of land cover. Continuous detection of anomalies is important for studying spatial-temporal processes of land cover changes. Although many time-series-analysis methods have been developed for change detection, to the best of our knowledge, few methods focus on continuously detecting...
Change detection is an important issue in many applications. In this study, we propose a new change detector for multi-temporal polarimetric synthetic aperture radar (SAR) images. The new detector is based on the optimization of polarimetric contrast minimization. The optimized solution of the contrast model is also given. Experiments are performed on two RADARSAT-2 data sets and results show agreement...
We propose a time-series model for characterizing land cover change. The model is developed based on a realistic view of land surface transformation—spatially discrete land cover change events are continuous processes over time. The model utilizes a non-linear mathematical function to reconstruct the underlying continuous land change process from temporally discrete satellite observations. Based on...
Remote sensing has been widely applied for environmental monitoring by means of change detection techniques, commonly for identifying deforestation signs which is the gateway for illegal activities such as uncontrolled urban growth and grazing pasture. Monthly acquired X-Band images from airborne Synthetic Aperture Radar (SAR) provided multi-temporal scenes employed in this work resulting in environmental...
This paper proposes a framework of change detection with multi-source remote sensing images through collaboration of multiple operators. Firstly, pre-processed images are distributed to different operators. Then the images are classified by the operators independently. Finally, with uploaded classification results, change detection result can be derived through evidential fusion based on PCR5 rule...
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