The sequential maximum angle convex cone (SMACC) endmember model

By John Gruninger, Anthony J. Ratkowski, Michael L. Hoke. SPIE Defense + Commercial Sensing, 2004

297 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/2b6f2d5c923c5e1fdb53db86b9c2144f7b6d24e6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Kian Pang Tan
2. Kasturi Devi Kanniah
3. Arthur Philip Cracknell

In Ecology

1. Kian Pang Tan
2. Kasturi Devi Kanniah
3. Arthur Philip Cracknell

In Media Technology

1. Kian Pang Tan
2. Kasturi Devi Kanniah
3. Arthur Philip Cracknell

Citations in the corpus

• Use of UK-DMC 2 and ALOS PALSAR for studying the age of oil palm trees in southern peninsular Malaysia (2013, methodology): “SMACC is a tool designed to find spectral endmembers and their abundances throughout an image.”
• Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, unknown): “• The sequential maximum angle convex cone (SMACC) algorithm [125] is based on a convex cone for representing the spectral vectors.”