Landslide mapping by textural analysis of ATM data
In this paper we evaluate 2 statistical approaches to semi-automated texture enhancement and discrimination for landslide mapping in semi-arid, sedimentary terrain from Daedalus Airborne Thematic mapper (ATM) data. A supervised texture discrimination technique is applied, based on calculating similarities between a reference texture spectrum obtained from training samples and spectra from moving image windows. The results are compared with those from interpreting a set of popular texture measures from the literature, derived from grey level co-occurrence matrix statistics. In this comparison, interpretation is facilitated by statistical selection of the best combination of 3 measures using a sequential forward search algorithm. It is concluded that the texture spectrum based discrimination technique proves superior to using pre-defined sets of texture measures, since it is able to highlight areas on imagery which are often associated with disrupted, displaced land masses.
Bibliographic Reference: Paper presented: Eleventh Thematic Conference and Workshops on Applied Geologic Remote Sensing, Ann Arbor (US), February 27 - March 2, 1996
Availability: Available from (1) as Paper EN 39527 ORA
Record Number: 199610186 / Last updated on: 1996-03-01
Original language: en
Available languages: en