-Shikha Gaur (Towards course project CS539 Fall 2017)

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Presentation transcript:

-Shikha Gaur (Towards course project CS539 Fall 2017) Super-resolution of Hyperspectral satellite images with the help of Hopfield Neural Network -Shikha Gaur (Towards course project CS539 Fall 2017)

Hyperspectral images Figure 1 : A representation of hyperspectral data cube (Adapted from http://en.wikipedia.org/) Figure 2 : Concept of hyperspectral imagery. Each pixel has an associated spectrum. (Adapted from http://www.accessscience.com) The disadvantage: poor spatial resolution of Hyperspectral sensors Hyperion sensor has 10 nm spectral resolution at the cost of 30 m spatial resolution!

Problems arising from poor spatial resolution Mixed Pixel Problem Solution to Mixed Pixel Problem Abundance fractions and sub-pixel map Figure 3 : Pure pixels and mixed pixels (adapted from Mianji et al., 2009) Figure 4 :Division of a pixel into sub-pixels(scale factor = 4) (adapted from Wu et al., 2011)

Super-resolution Algorithmic approaches for increasing spatial resolution Spatial contiguity principle Figure 5 : Tobler’s first law of geography (Adapted from http://geohealthinnovations.org/our-mission/)

Hopfield neural network A fully connected recurrent network used as energy minimization tool Figure 6 : A Hopfield neural network (Adapted from http://en.wikipedia.org/)

Figure 7 : Flow chart of the algorithm Proposed algorithm Figure 7 : Flow chart of the algorithm

Results – Reference input data Fuzzy C-means Clustering Figure 8 : The study area (adapted from http://earthexplorer.usgs.gov/) Figure 9 : Hyperion image of 1 band of the study area (adapted from http://earthexplorer.usgs.gov/) Figure 10 : Reference LULC map obtained by applying FCM to Hyperion image (30m resolution)

Results – input and output (zoom factor = 3) Figure 11 : Low resolution input image Figure 12 : Super-resolved output image

Results – input and output (zoom factor = 5) Figure 13 : Low resolution input image Figure 14 : Low resolution input image

Discussion and potential for further work The classification accuracy and execution time turned out to be better than the expected values. Zoom factor of 10 was also tested and resulted in 66.7% accuracy and the program execution time was 86 seconds. The accuracy can be improved by improving the convergence criteria. For final classification results, in case of conflict between multiple classes, we chose the class with highest membership value. This can be improvised where we also take the current output of HNN into account (using a variant of SoftMax layer). In the current setup SoftMax led to image noise and less classification accuracy.

Thank you! Any questions?