Qin Dai

An improved Bag-of-Words framework for remote sensing image retrieval in large-scale image databases

Due to advances in satellite and sensor technology, the number and size of Remote Sensing (RS) images continue to grow at a rapid pace. The continuous stream of sensor data from satellites poses major challenges for the retrieval of relevant information from those satellite datastreams. The Bag-of-Words (BoW) framework is a leading image search approach and has been successfully applied in a broad range of computer vision problems and hence has received much attention from the RS community. However, the recognition performance of a typical BoW framework becomes very poor when the framework is applied to application scenarios where the appearance and texture of images are very similar. In this paper, we propose a simple method to improve recognition performance of a typical BoW framework by representing images with local features extracted from base images. In addition, we propose a similarity measure for RS images by counting the number of same words assigned to images. We compare the performance of these methods with a typical BoW framework. Our experiments show that the proposed method has better recognition performance than that of the BoW and requires less storage space for saving local invariant features.

An improved SVM model for relevance feedback in remote sensing image retrieval

With the rapid development of satellite remote sensing technology and an ever-increasing number of Earth observation satellites being launched, the global volume of remotely sensed imagery has been growing exponentially. Processing the variety of remotely sensed data has increasingly been complex and difficult. It is also hard to efficiently and intelligently retrieve what users need from a massive database of images. This paper introduces an improved support vector machine (SVM) model, which optimizes the model parameters and selects the feature subset based on the particle swarm optimization (PSO) method and genetic algorithm (GA) for remote sensing image retrieval. The results from an image retrieval experiment show that our method outperforms traditional methods such as GRID, PSO, and GA in terms of consistency and stability.

Location-based instant satellite image service: concept and system design

This paper presents various limitations of the current remote sensing data distribution models and proposes a new concept called the location-based instant satellite image service for a new generation of remote sensing image distribution system. The essential feature of the service is that customers can subscribe to data based on the location of interest and satellite image data received by antenna will be distributed to customers terminal devices instantly after imaging over the subscribed area. The workflow, architecture, and key technologies of the new generation data distribution system are described. The system is composed of four parts: data comprehensive processing component, data management component, product distribution component, and data display component. Based on this, a prototype system is developed, which demonstrates the promising service model with great potential for increased usage in many applications.

Study of Burn Scar Extraction Automatically Based on Level Set Method using Remote Sensing Data

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model.

Remote Sensing Image Change Detection Based on Swarm Intelligent Algorithm

For several decades the remote sensing change detection methods for depicting land cover have gained a great achievements, and the artificial intelligence techniques have being applied to remote sensing data processing and show great potential. Ant colony optimization (ACO) and Particle swarm optimization (PSO) as the two main algorithms of swarm intelligence, and because of the self-organization, cooperation, communication and other intelligent merits, they have great potential in research area. This paper introduces remotely sensed image change detection using the swarm intelligent algorithm. Selecting Landsat-5 TM image located in Beijing area as experimental data, this paper use the algorithm for constructing the change rules, then use these rules to process the example data, the experiment results show that warm intelligent algorithm has provided a new method for remote sensing image change detection.

A Content-Based Remote Sensing Image Change Information Retrieval Model

With the rapid development of satellite remote sensing technology, the size of image datasets in many application areas is growing exponentially and the demand for Land-Cover and Land-Use change remote sensing data is growing rapidly. It is thus becoming hard to efficiently and intelligently retrieve the change information that users need from massive image databases. In this paper, content-based image retrieval is successfully applied to change detection, and a content-based remote sensing image change information retrieval model is introduced. First, the construction of a new model framework for change information retrieval from a remote sensing database is described. Then, as the target content cannot be expressed by one kind of feature alone, a multiple-feature, integrated retrieval model is proposed. Thirdly, an experimental prototype system that was set up to demonstrate the validity and practicability of the model is described. The proposed model is a new method of acquiring change detection information from remote sensing imagery and so can reduce the need for image pre-processing and also deal with problems related to seasonal changes, as well as other problems encountered in the field of change detection. Meanwhile, the new model has important implications for improving remote sensing image management and autonomous information retrieval. The experiment results obtained using a Landsat data set show that the use of the new model can produce promising results. A coverage rate and mean average precision of 71% and 89%, respectively, were achieved for the top 20 returned pairs of images.

Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake

Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.

The analysis of East Dongting lake water change based on time series of remote sensing data

Long time series of remote sensing data (such as MODIS, NOAA/AVHRR, SPOT/VEGETATION data) as an important source of research data, has been widely applied on vegetation growth monitoring, phenology information extraction and land use monitoring and other fields. In this paper, we take MODIS time-series data in the East-Dongting lake area as experimental remote sensing data. Firstly, long time series of remote sensing data will be gotten through features exaction. Then, the time series data will be analyzed by the hierarchical clustering method. Lastly, this paper presented a new hierarchical clustering method with class number automatic calculation, and according to the characteristics of the time series data, the dynamic time distance (DTW) instead of traditional Euclidean distance were used in the clustering method. The experimental results show that the proposed algorithm can well solve the problems of time axis stretching, bending, linear drifting and etc. And, its clustering effect is more significant.

A Novel ACO Based Land Cover Classification Approach Using Optical and SAR Data

 Abstract—The land cover classification methods based on statistical theory using remote sensing data have great achievements for the last several decades, but they have exposed some weaknesses in dealing with multi-source and multi-dimensional data. Ant colony optimization (ACO), as an excellent intelligent algorithm, has been applied on many research fields for solving optimization issues. How to solve the optimization problem of sampling data is the key step in process of land cover classification with multi-source and multi-dimensional data, so ACO algorithm has many potential advantages in the field of remote sensing data processing. In this paper, an intelligent method is developed for classifying the land cover types combining Landsat TM data with Envisat ASAR data on the basis of ACO algorithm. For identifying the classification precision of land cover with ACO algorithm, we respectively compare it with the results by Maximum Likelihood Classification (MLC) and decision tree C4.5. The comparing results show that ACO algorithm can well take advantages of optical and radar data to improve classification precision and select the best features subsets to construct simpler rules.