@article{oai:jaxa.repo.nii.ac.jp:00022446,
 author = {橋本, 秀太郎 and 田殿, 武雄 and 小野里, 雅彦 and 堀, 雅裕 and 塩見, 慶 and Hashimoto, Shutaro and Tadono, Takeo and Onosato, Masahiko and Hori, Masahiro and Shiomi, Kei},
 issue = {2},
 journal = {日本リモートセンシング学会誌, Journal of The Remote Sensing Society of Japan},
 month = {Oct},
 note = {Here we propose an accurate and robust method for large-area land-use and land-cover (LULC) mapping using multi-temporal 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 multi-temporal classification results. In the per-scene classification, we accurately estimated the class-conditional spectral-seasonal 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., 資料番号: PA1410087000},
 pages = {102--112},
 title = {多時期光学観測データを用いた高精度土地被覆分類手法の開発},
 volume = {34},
 year = {2014}
}