Land-cover change detection using multi-temporal MODIS NDVI data

Monitoring the locations and distributions of land-cover changes is important for establishing links between policy decisions, regulatory actions and subsequent land-use activities. Past studies incorporating two-date change detection using Landsat data have tended to be performance limited for applications in biologically complex systems. This study explored the use of 250 m multi-temporal MODIS NDVI 16-day composite data to provide an automated change detection and alarm capability on a 1 year time-step for the Albemarle-Pamlico Estuary System (APES) region of the US. Detection accuracy was assessed for 2002 at 88%, with a reasonable balance between change commission errors (21.9%), change omission errors (27.5%), and Kappa coefficient of 0.67. Annual change detection rates across the APES over the study period (2002-2005) were estimated at 0.7% per annum and varied from 0.4% (2003) to 0.9% (2004). Regional variations were also readily apparent ranging from 1.6% to 0.1% per annum for the tidal water and mountain ecological zones, respectfully. This research included the application of an automated protocol to first filter the MODIS NDVI data to remove poor (corrupted) data values and then estimate the missing data values using a discrete Fourier transformation technique to provide high-quality uninterrupted data to support the change detection analysis. The methods and results detailed in this article apply only to non-agricultural areas. Additional limitations attributed to the coarse resolution of the NDVI data included the overestimation of change area that necessitated the application of a change area correction factor.     

On change detection and displacement vector estimation in image sequences

Recent developments in the areas of displacement vector estimation as well as dissimilarity grading by a maximum likelihood ratio can be related to each other quantitatively in such a way that dissimilarity grading is reduced to interframe displacement estimation.     

Retrospective seagrass change detection in a shallow coastal tidal Australian lake

Satellite imagery was used to detect the change in seagrass and macroalgal communities of a shallow coastal lake over a period of 14 years. The lake benthic material was classified into sets of spectral classes representing the patterns and texture of the ecosystem, and then linked to environmentally relevant labels through a radiative transfer model. The classification results for 2002 achieved an accuracy of 76% for the least understood areas; other areas were significantly better, but not quantified. Classification results of 1988, 1991, and 1995 were consistent with past surveys and maps. Based on the change detection from 1988 to 2002 Posidonia, Ruppia and Halophila change slightly in the 14 year period from 1988 to 2002. However, Zostera has undergone significant change and adaptation. Early in the time series (between 1988 and 1991) a reduction in Zostera beds was evident, especially in the middle and south of the lake with some areas not returning by 2002. Epiphytic growth over Zostera could be a confounding factor here, but the Landsat sensors do not have sufficient spectral resolution to detect these subtleties. Hyperspectral remote sensing could resolve this issue more clearly.     

Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods

The recent release of the U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001, which represents the nation's land cover status based on a nominal date of 2001, is widely used as a baseline for national land cover conditions. To enable the updating of this land cover information in a consistent and continuous manner, a prototype method was developed to update land cover by an individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season in 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, land cover classifications at the full NLCD resolution for 2006 areas of change were completed by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain several metropolitan areas including Seattle, Washington; San Diego, California; Sioux Falls, South Dakota; Jackson, Mississippi; and Manchester, New Hampshire. Results from the five study areas show that the vast majority of land cover change was captured and updated with overall land cover classification accuracies of 78.32%, 87.5%, 88.57%, 78.36%, and 83.33% for these areas. The method optimizes mapping efficiency and has the potential to provide users a flexible method to generate updated land cover at national and regional scales by using NLCD 2001 as the baseline.     

Semi-supervised change detection using modified self-organizing feature map neural network

In the present article, semi-supervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In the proposed methodology, training of the MSOFM network is initially performed using only a few labeled patterns. Thereafter, the membership values, in both the classes, for each unlabeled pattern are determined using the concept of fuzzy set theory. The soft class label for each of the unlabeled patterns is then estimated using the membership values of its K nearest neighbors. Here, training of the network using the unlabeled patterns along with a few labeled patterns is carried out iteratively. A heuristic method has been suggested to select some patterns from the unlabeled ones for training. To check the effectiveness of the proposed methodology, experiments are conducted on three multi-temporal and multi-spectral data sets. Performance of the proposed work is compared with that of two unsupervised techniques, a supervised technique and two semi-supervised techniques. Results are also statistically validated using paired t-test. The proposed method produced promising results.     

Optimizing the binary discriminant function in change detection applications

Binary discriminant functions are often used to identify changed area through time in remote sensing change detection studies. Traditionally, a single change-enhanced image has been used to optimize the binary discriminant function with a few (e.g., 5-10) discrete thresholds using a trial-and-error method. Im et al. [Im, J., Rhee, J., Jensen, J. R., & Hodgson, M. E. (2007). An automated binary change detection model using a calibration approach. Remote Sensing of Environment, 106, 89-105] developed an automated calibration model for optimizing the binary discriminant function by autonomously testing thousands of thresholds. However, the automated model may be time-consuming especially when multiple change-enhanced images are used as inputs together since the model is based on an exhaustive search technique. This paper describes the development of a computationally efficient search technique for identifying optimum threshold(s) in a remote sensing spectral search space. The new algorithm is based on “systematic searching.” Two additional heuristic optimization algorithms (i.e., hill climbing, simulated annealing) were examined for comparison. A case study using QuickBird and IKONOS satellite imagery was performed to evaluate the effectiveness of the proposed algorithm. The proposed systematic search technique reduced the processing time required to identify the optimum binary discriminate function without decreasing accuracy. The other two optimizing search algorithms also reduced the processing time but failed to detect a global maxima for some spectral features.     

基于状态转移矩阵的变化检测方法

土地利用变化检测一直是遥感影像变化检测研究的重点。在分析已有变化检测方法的基础上提出了一种基于状态转移矩阵（State Transition Matrix,STM）的变化检测方法。以像斑为分析单位,通过样本像斑的选择与更新对变化检测区域进行分析,利用辅助数据生成的地物变化状态转移矩阵对变化检测结果进行修正。实验证明了该方法的有效性。     

An enhanced Web page change detection approach based on limiting similarity computations to elements of same type

This paper describes an efficient Web page detection approach based on restricting the similarity computations between two versions of a given Web page to the nodes with the same HTML tag type. Before performing the similarity computations, the HTML Web page is transformed into an XML-like structure in which a node corresponds to an open-closed HTML tag. Analytical expressions and supporting experimental results are used to quantify the improvements that are made when comparing the proposed approach to the traditional one, which computes the similarities across all nodes of both pages. It is shown that the improvements are highly dependent on the diversity of tags in the page. That is, the more diverse the page is (i.e., contains mixed content of text, images, links, etc.), the greater the improvements are, while the more uniform it is, the lesser they are.     

Exploiting processor features to implement error detection in reduced precision matrix multiplications

Modern processors incorporate complex arithmetic units that can work with large word-lengths. Those units are useful for applications that require high precision. There are however, many applications for which the use of reduced precision is sufficient. In those cases, one possibility is to use the large word-length arithmetic units to implement reduced precision operations with additional error detection. In this paper, this idea is explored for the case of matrix multiplications. A technique is presented and evaluated. The results show that it can detect most errors and that for large matrixes the overhead in terms of execution time is small.     

基于场景检测的自适应Intra帧差错掩盖算法

为了减小视频传输中的错误对解码端重建视频质量的影响,提出了一种基于场景检测的自适应Intra帧差错掩盖算法。该算法利用H.264/AVC的帧内预测模式分布差异检测Intra帧的场景切换,对非场景切换的Intra帧进行空时域掩盖的失真度估算,在此基础上进行自适应的空时域掩盖。实验结果表明,该算法充分利用了Intra帧的时域相关性,与已有的Intra帧差错掩盖算法比较,该算法恢复的视频序列平均峰值信噪比提高了1~2 dB,具有更好的差错掩盖效果。