A scalable spatiotemporal inference framework based on statistical shape analysis for natural ecosystem monitoring by remote sensing

Modeling the dynamic interactions within an ecosystem and among ecosystems is essential to the sustainability of the biosphere and remote sensing is a proven technology that can effectively map and characterize cultural and natural landscapes. However, the lack of scalable and reliable algorithms and associated implementations to extract implicit patterns in remotely sensed images has severely limited its success. In this paper, based on a key observation that multiple samples of multispectral sensing form a curve in the spectral space (named as mspectral curve), we propose a novel mathematical change detection and land cover classification framework based on statistical shape analysis for natural ecosystem monitoring using remote sensing. By bridging multitemporal analysis and statistical shape analysis, a rich set of robust estimation and optimization techniques can be utilized via tangent space representation to classify land cover and detect changes. The framework also introduces scalable clustering algorithms based on product quantization and residual vector quantization to deal with the labeling problem. In order to deal with clouds and cloud shadows, an adapted Fmask (Function of mask) method is used. Deep network classification is also incorporated for more efficient and accurate context-sensitive land cover classification. The framework provides a fundamental building block to improve semantic classification and change detection using temporal and spatial context to disambiguate spectral classes into information classes.