An Object-Based Linear Weight Assignment Fusion Scheme to Improve Classification Accuracy Using Landsat and MODIS Data at the Decision Level
An Object-Based Linear Weight Assignment Fusion Scheme to Improve Classification Accuracy Using Landsat and MODIS Data at the Decision Level
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DOI：
10.1109/tgrs.2017.2737780
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发表时间：
2017-09
影响因子：
8.2
通讯作者：
X. Guan;Gaohuan Liu;Chong Huang;Qingsheng Liu;Chunsheng Wu;Yan Jin;Yafei Li
中科院分区：
文献类型：
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作者：
X. Guan;Gaohuan Liu;Chong Huang;Qingsheng Liu;Chunsheng Wu;Yan Jin;Yafei Li
大地卫星图像由于其相对较高的空间分辨率而广泛用于土地利用研究。然而，可用的数据集的数量是有限的，相对较长的重访间隔和物候效应可以显着降低分类精度。中分辨率成像光谱仪（MODIS）图像具有较高的时间频率，可以提供额外的时间序列信息。然而，他们的能力有限，由于其粗糙的空间分辨率分类异质景观。不同数据源的融合是改进土地覆盖分类的一个潜在解决办法。提出了一种联合收割机Landsat和MODIS遥感数据决策级融合方案。首先，对两种遥感数据进行多分辨率分割，以识别景观对象，并在后续步骤中作为融合单元。然后，模糊分类应用于两个不同分辨率的数据集和分类精度进行评估。根据两个数据集在分类评价中的表现，在最终的分类决策中实现了一种简单的基于成像对象隶属度加权和的权重分配技术。加权因子的计算基于混淆矩阵和检测到的土地覆盖的异质性。该算法能够将MODIS数据的时间序列光谱信息与从陆地卫星数据中提取的空间背景相结合，从而提高土地覆盖分类的准确性。与单独的Landsat和MODIS数据相比，使用融合技术的总体分类精度分别提高了7.43%和10.46%。
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.