Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that are the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Landsat satellite images are extensively used in land-use studies due to their relatively high spatial resolution. However, the number of usable data sets is limited by the relatively long revisit interval and phenology effects can significantly reduce classification accuracy. Moderate Resolution Imaging Spectroradiometer (MODIS) images have higher temporal frequency and can provide extra time-series information. However, they are limited in their capability to classify heterogeneous landscapes due to their coarse spatial resolution. Fusion of different data sources is a potential solution for improving land-cover classification. This paper proposes a fusion scheme to combine Landsat and MODIS remote sensing data at the decision level. First, multiresolution segmentations on the two kinds of remote sensing data are performed to identify the landscape objects and are used as fusion units in subsequent steps. Then, fuzzy classifications are applied to each of the two different resolution data sets and the classification accuracies are evaluated. According to the performance of the two data sets in classification evaluation, a simple weight assignment technique based on the weighted sum of the membership of imaged objects is implemented in the final classification decision. The weighting factors are calculated based on a confusion matrix and the heterogeneity of detected land cover. The algorithm is capable of integrating the time-series spectral information of MODIS data with spatial contexts extracted from Landsat data, thus improving the land-cover classification accuracy. The overall classification accuracy using the fusion technique increased by 7.43% and 10.46% compared with the results from the individual Landsat and MODIS data, respectively.

This paper studies the problem of categorical data clustering, especially for transactional data characterized by high dimensionality and large volume. Starting from a heuristic method of increasing the height-to-width ratio of the cluster histogram, we develop a novel algorithm -- CLOPE, which is very fast and scalable, while being quite effective. We demonstrate the performance of our algorithm on two real world datasets, and compare CLOPE with the state-of-art algorithms.