Detecting land use and land cover changes is critical to monitor natural resources and analyze global environmental changes. In this paper, we investigate the land cover detection using the remote sensing data from earth-observing satellites. Due to the natural disturbances, e.g., clouds and aerosoles, and the data acquisition errors by devices, remote sensing data frequently contain much noise. Also, many land covers cannot be easily identified in most dates of a year. Instead, they show distinctive temporal patterns only during certain period of a year, which is also referred to as the discriminative period. To address these challenges, we propose a novel framework which combines the spatial context knowledge with the LSTM-based temporal modeling for land cover detection. Specifically, the framework learns the spatial context knowledge selectively from its neighboring locations. Then we propose two approaches for discriminative period detection based on multi-instance learning and local attention mechanism, respectively. Our evaluations in two real-world applications demonstrate the effectiveness of the proposed method in identifying land covers and detecting discriminative periods.
|Original language||English (US)|
|Title of host publication||SIAM International Conference on Data Mining, SDM 2019|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2019|
|Event||19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada|
Duration: May 2 2019 → May 4 2019
|Name||SIAM International Conference on Data Mining, SDM 2019|
|Conference||19th SIAM International Conference on Data Mining, SDM 2019|
|Period||5/2/19 → 5/4/19|
Bibliographical noteFunding Information:
This work was funded by the NSF Awards 1029711 and DTC seed grant. Access to computing facilities was provided by Minnesota Supercomputing Institute.