Anzhi Yue

Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing

In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM) trained for each land cover class. Assuming a pixel’s initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient.

Satellite Image Time Series Decomposition Based on EEMD

Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD) is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs). EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD). Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), and Global Environment Monitoring Index (GEMI) time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.

A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data

Fire detection based on multi-temporal remote sensing data is an active research field. However, multi-temporal detection processes are usually complicated because of the spatial and temporal variability of remote sensing imagery. This paper presents a spatio-temporal model (STM) based forest fire detection method that uses multiple images of the inspected scene. In STM, the strong correlation between an inspected pixel and its neighboring pixels is considered, which can mitigate adverse impacts of spatial heterogeneity on background intensity predictions. The integration of spatial contextual information and temporal information makes it a more robust model for anomaly detection. The proposed algorithm was applied to a forest fire in 2009 in the Yinanhe forest, Heilongjiang province, China, using two-month HJ-1B infrared camera sensor (IRS) images. A comparison of detection results demonstrate that the proposed algorithm described in this paper are useful to represent the spatio-temporal information contained in multi-temporal remotely sensed data, and the STM detection method can be used to obtain a higher detection accuracy than the optimized contextual algorithm.

New normalized difference index for built-up land enhancement using airborne visible infrared imaging spectrometer imagery

Abstract In remote sensing imagery, various normalized difference indices are widely used for land cover mapping. Each index has its targeting cover type with a specialized data source. However, these indices are generally only studied in multispectral data. Hyperspectral images have become increasingly attractive due to their richness of spectrum information. A new index, i.e., Normalized Difference Built-up Index for Hyperspectral data (NDBIh), oriented to built-up land enhancement in hyperspectral remote sensing data is proposed. Spectral response curves of different cover types and possible calculation equations for NDBIh are obtained first. The equation having the best ability to differentiate built-up land from other areas is referred to as NDBIh. To evaluate the ability of our NDBIh, two other built-up indices, the conventional Normalized Difference Built-Up Index (NDBI) and the Index-based Built-Up Index (IBI), are compared with NDBIh both qualitatively and quantitatively. Experiments on airborne visible infrared imaging spectrometer data indicate that the NDBIh outperforms NDBI and IBI in identifying built-up land.

Decision tree coupled with feature optimization for object-based classification of ZY-1-02C satellite images

The Separability and Thresholds (SEaTH) algorithm calculates the the SEparability and the corresponding THresholds of object classes for any number of given features. However, it is applicable only to the normally distributed training data. To cope with the problem, The Classification And Regression Tree (CART) coupled with SEaTH for object-based classification approach is proposed in the paper. The idea of this method is derived from the merits of the CART which can effectively analyze the non-normally distributed data and automatically create the classification tree. A comparison of classification results demonstrate that the solution for object-based classification proposed in this article can be used to obtain a higher classification accuracy than SEaTH classification.

Practical Bottom-up Golf Course Detection Using Multispectral Remote Sensing Imagery

The rapid growth of golf course has constituted a nonnegligible threat to conservation of cropland and water resource in China. To monitor golf course at a large scale with low cost, a practical bottom-up golf course detection approach using multispectral remote sensing imagery is proposed. First of all, turfgrass, water-body and bunker are determined as the basic elements based on analyzing golf course land-use characteristics. Secondly, turfgrass and water-body are extracted using spectral indexes and these two basic elements are combined as region-of-interest under guidance of prior-knowledge. Afterwards, bunker is extracted by spectral mixture analysis restricted to region-of-interest. Finally, fuzzy C-means is adopted to recognize golf course using landscape metrics. A SPOT-5 HRG multispectral image of Beijing is used to validate the proposed method, and detection rate and false alarm rate are 86.67% and 38.10% respectively.

Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data

Abstract. The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.

Urban new construction land parcel detection with normalized difference vegetation index and PanTex information

Abstract. An urban new construction land parcel detection method based on normalized difference vegetation index (NDVI) and built-up area presence index is proposed for high-resolution remote sensing images. The method consists of three main steps: construction land detection using NDVI and PanTex, false change removal, and new construction land parcel extraction. More specifically, a change proportion index is raised to convert the pixel-based change detection map to parcels in combination with a segmentation process. From experimental results validated using two cases of high-resolution optical satellite images, the proposed method is demonstrated to be efficient and achieves a per-object overall accuracy rate beyond 95%, significantly superior to the traditional postclassification change detection method. Furthermore, the proposed method avoids errors resulting from classification in the method of postclassification comparison.

Vehicles detection using GF-2 imagery based on watershed image segmentation

Road traffic volume monitoring plays an important role in transportation planning and spatial development, particularly in urban areas. The high-resolution satellite imagery provides a new data source to detect vehicles. Meanwhile, Satellite image covers large areas instantaneously, providing a possibility for snapshotting road traffic conditions. In this paper, we proposed an approach based on watershed image segmentation to detect the urban road vehicles from GF-2 imagery. The vehicles detection involves the two main steps: Firstly, a GIS road vector map and vegetation masks were applied to the image to guide vehicle detection by restricting the roads only. Secondly, watershed image segmentation was performed to separate bright and dark vehicles from the background in the road region. Then, a rule-based classifier was established to classify the image objects into the vehicle and the non-vehicle objects by using the spectral and shape feature information of image objects. Finally, the overall performance of the vehicle detection were compared with the manually counts, yielding overall accuracy of 81% with 93% classification accuracy. This detection accuracy may be considered acceptable for operational use in traffic monitoring.

Improved snow cover monitoring method based on HJ-1B infrared data

Monitoring snow distribution area plays an important role in researching climate change and energy exchange process. Chinese small satellite constellation (abbreviated HJ constellation) is special for environment and disaster continuously monitoring or forecasting. By using HJ-1B CCD and infrared data or only using its infrared data, it can construct NDSI (Normalized Difference Snow Index) or MNDSI (Modified Normalized Difference Snow Index) respectively to detect snow area. However, obtaining the CCD and infrared images at the same time is impossible for the long time monitoring. Furthermore, snow area is mixed with vegetation, the snow indexs value will be reduced, and snow area in these places could not be detected accurately. Therefore, this paper introduces an improved snow cover monitoring method based on MNDSI and priori information of vegetation to increase the detection accuracy of snow area by using HJ-1B infrared image. The proposed method has the higher precision than the compared method.