  Time series data of urban land cover is of great utility in analyzing urban
growth patterns, changes in distribution of impervious surface and vegetation
and resulting impacts on urban micro climate. While Landsat data is ideal for
such analysis due to the long time series of free imagery, traditional
per-pixel hard classification fails to yield full potential of the Landsat
data. This paper proposes a sub-pixel classification method that leverages the
temporal overlap of Landsat-5 TM and Resourcesat-1 LISS-IV sensors. We train a
convolutional neural network to predict fractional land cover maps from 30m
Landsat-5 TM data. The reference land cover fractions are estimated from a
hard-classified 5.8m LISS-IV image for Bengaluru from 2011. Further, we
demonstrate the generalizability and superior performance of the proposed model
using data for Mumbai from 2009 and comparing it to the results obtained using
a Random Forest classifier. For both Bengaluru (2011) and Mumbai (2009) data,
Mean Absolute Percentage Error of our CNN model is in the range of 7.2 to 11.3
for both built-up and vegetation fraction prediction at the 30m cell level.
Unlike most recent studies where validation is conducted using data for a
limited spatial extent, our model has been trained and validated using data for
the complete spatial extent of two mega cities for two different time periods.
Hence it can reliably generate 30m built-up and vegetation fraction maps from
Landsat-5 TM time series data to analyze long term urban growth patterns.
