SBIR-STTR Award

Increased spatial and temporal resolution of remotely sensed multispectral imagery is crucial for improved monitoring of land surface dynamics in heterogeneous landscapes undergoing rapid change. Given orbital constraints, satellite imaging sensors such as MODIS and Landsat 8 OLI exhibit tradeoffs between frequent/coarse and sparse/fine scenes, and spatiotemporal fusion techniques have been developed to synthesize images with improved spatial and temporal resolutions from such complementary satellite pairs. In contrast, imagery from manned fixed wing aircraft and UAVs can be acquired both frequently and at high resolution over limited areas. Land surface monitoring would greatly benefit from a capability to combine imagery from these disparate platforms, for which inconsistent or irregular revisit times and variabilities in resolution and spectral bands make existing spatiotemporal fusion techniques insufficient to combine them effectively. This project will exploit these recent machine learning advances to combine imagery from disparate satellite and airborne platforms, using multi-resolution image time series and transferring fine resolution knowledge gained from higher resolution training images to lower-resolution test scenes. We will test the feasibility of the system to provide improved classification of vegetative land cover and estimations of fractional vegetation cover, particularly for agricultural areas that frequently change on a small spatial scale. During Phase I, we will use an unmanned aerial vehicle (UAV) to make weekly multispectral image collects during the growing cycle of several agricultural crops and combine the scenes with Landsat 8 OLI and Sentinel 2 satellite imagery. We will spatially and temporally subsample the high resolution UAV imagery to simulate imagery acquired from a variety of aerial and additional satellite platforms and compare classifier performance for different spatial resolutions and repeat periods. Potential NASA Applications (Limit 1500 characters, approximately 150 words) Related follow-on opportunities for NASA program infusion include integration with the TOPS-SIMs irrigation management program at the Ecological Forecasting Lab at NASA Ames, and NASA Goddard’s Harvest consortium led by the University of Maryland to enhance the use of satellite data in decision making related to food security and agriculture. We will also target its use in more general land cover, land use and change (LCLUC) classification applications such as earth system simulations at the NASA Center for Climate Simulation. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) Fresh vegetable industry regional forecasting of amounts of different specialty crops.

We propose to develop and commercialize a deep learning-based image classification capability that detects fine-scale and rapidly changing land surface features, using relatively low resolution and low-cost imagery and an architecture that is simple and fast to train. The proposed system promises to substantially improve the study of high frequency land cover dynamics in heterogeneous landscapes by addressing two principal roadblocks to higher spatial resolution and more frequent land cover classification: 1) the high cost of acquiring high resolution multispectral imagery on a frequent basis, and 2) the general complexity of using machine learning techniques to improve classification capabilities. Our innovation involves using time series of multispectral imagery with relatively rich spectral content as a trade-off with spatial resolution, and applying it on a pixel by pixel basis. Our Phase II focus will be on agricultural areas that frequently change on a small scale. Annual vegetable crops are a key set of relevant land cover classes. But our methodology is extensible to other land cover types, such as urban settlements and their change, and other data inputs in addition to imagery, such as time series of weather data. Potential NASA Applications (Limit 1500 characters, approximately 150 words) Related follow-on opportunities for NASA program infusion include integration with the TOPS-SIMs irrigation management program at the Ecological Forecasting Lab at NASA Ames, and NASA Goddard’s Harvest Consortium led by the University of Maryland to enhance the use of satellite data in decision making related to food security and agriculture, and the Surface Biology and Geology (SBG) Decadal Designated Observable Study. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) Related commercialization opportunities include monitoring and forecasting for industrial agriculture, particularly for fresh vegetable crops, improved cropland classification for USDA’s Cropland Data Layer, and food waste and sustainability applications addressing prioritized actions of the EPA, USDA and FDA.