Architecture Tweaking Image Analysis S/W for Automated Identification of Land Features in Satellite Images

Overview

To dynamically track environmental changes, we need an automated method that identifies land features like forest health etc. from satellite images. Existing softwares (S/W) rely on professionals to analyse graphs, supply patterns or locate important objects in images, making it time consuming & often inaccurate. My S/W, enabled by its novel architecture tweaking methodology, identifies land features in Landsat images without any human analysis. My S/W is not supplied with fixed algorithms(algos). Instead it is only supplied with self-adaptable algos: basic structures of image analysis algos that aren’t algos on their own. When my S/W is trained with data, instead of learning the training values, it tweaks the architecture of base algos by changing their structure & combining them to form entirely new algos. These algos are generated in the training phase using a variant of the Monte Carlo method as a guide. The self-created algos combine simulated false colour images, clustering & texture analysis. This S/W opens research avenues by finding patterns geographers usually don’t look for. For eg.To determine vegetation type, instead of finding canopy density, my S/W clustered bare soil gaps between canopies by drawing obtuse triangles & stretching them. My S/W identified 4 land features with 96% accuracy. Being automated & accurate, it can dynamically track environmental changes. When trained with images of biopsy tissues, my S/W(with same base algos) performed cancer diagnosis, as under suitable restrictions, this task resembles the Landsat image analysis my S/W did.

Competition history

• ISEF 2018 Systems Software · Entry SOFT049

Resources

• ISEF abstract page ↗

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