A New Method to Derive Precise Land-use and Land-cover Maps Using Multi-temporal Optical Data
A New Method to Derive Precise Land-use and Land-cover Maps Using Multi-temporal Optical Data
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DOI：
10.11440/rssj.34.102
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发表时间：
2014-04
影响因子：
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通讯作者：
Shutaro Hashimoto;T. Tadono;M. Onosato;M. Hori;K. Shiomi
中科院分区：
文献类型：
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作者：
Shutaro Hashimoto;T. Tadono;M. Onosato;M. Hori;K. Shiomi
在这里，我们提出了一个准确的和强大的方法，大面积的土地利用和土地覆盖（LULC）映射使用多时相光学数据。传统的土地利用变化分类方法通常采用定期的时间序列数据来考虑土地利用变化的季节性。然而，高分辨率光学数据具有相当大的季节性偏差，因此难以使用时间序列数据。我们使用高分辨率光学卫星数据对LULC进行准确分类的基本想法是首先考虑季节性对每个场景进行分类，然后整合多时相分类结果。在每场景分类中，我们通过进行核密度估计（KDE）从训练数据中准确地估计了观测值的类条件谱季节密度，并且我们使用贝叶斯推理中的密度来获得类后验概率。在每个场景的多时间分类之后，我们通过整合多时间场景中的类后验概率来计算分类得分。我们使用高级可见光和近红外辐射计2型（AVNIR-2）低云层覆盖数据的1，876个场景，以10 m的空间分辨率对日本全国进行了8级分类，并通过进行交叉验证测试并将结果与现有方法进行比较，评估了分类的准确性：最大似然分类器（MLC）和支持向量机（SVM）。评估结果表明，所提出的方法的整体精度是最好的所有的方法检查。
Here we propose an accurate and robust method for large-area land-use and land-cover (LULC) mapping using multitemporal optical data. The conventional method for LULC classification usually uses time-series data at regular intervals to consider the seasonality of LULC. However, high-resolution optical data have considerable seasonal biases, making it difficult to use time-series data. Our basic idea for the accurate classification of LULC using high-resolution optical satellite data is to implement a classification for each scene considering seasonality first, and to then integrate multitemporal classification results. In the per-scene classification, we accurately estimated the class-conditional spectralseasonal densities of observation values from training data by conducting a kernel density estimation (KDE), and we used the densities in a Bayesian inference to obtain the class posterior probability. After the multi-temporal per-scene classification, we calculated the classification score by integrating class posterior probabilities in multi-temporal scenes. We conducted an 8-class classification for the entirety of Japan with 10-m spatial resolution using 1,876 scenes from the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) low-cloud-cover data, and we evaluated the accuracy of the classification by conducting a cross validation test and comparing the results to that obtained with existing methods: maximum likelihood classifier (MLC) and support vector machines (SVMs). The evaluation results showed that the overall accuracy of the proposed method is the best of all of the methods examined.