Paper Info
Title
Contextual clustering for image labeling: an application to degraded forest assessment in Landsat TM images of the Brazilian Amazon
Abstract
The modified adaptive pappas clustering (MPAC) algorithm, previously published in the image processing literature, is proposed as a valuable tool in the analysis of remotely sensed images where texture information is negligible. Owing to its contextual, adaptive, and multiresolutional labeling approach, MPAC preserves genuine but small regions, is easy to use (i.e., it requires minor user interaction to run), and is robust to changes in input parameters. As an application example, an MPAC-based three-stage classifier is applied to degraded forest detection in Landsat Thematic Mapper (TM) scenes of the Brazilian Amazon, where intermediate states of forest alterations caused by anthropogenic activities can be characterized by image structures 1-3 pixels wide. In three TM images of the Para test site, where classification results are validated by means of qualitative and quantitative comparisons with aerial photos, degraded forest areas cover 13% to 45% of the image ground coverage. In the Mato Grosso test site, the degraded forest class overlaps with 1) 10% of the closed-canopy forest detected by the deforestation mapping program of the Food and Agriculture Organization (FAO, 1992), and 2) 19% of the closed-canopy forest detected by the Tropical Rain Forest Information Center (TRFIC, 1996). These figures are in line with the conclusions of a study where present estimates of annual deforestation for the Brazilian Amazon are speculated to capture less than half of the forest area that is actually impoverished each year.
Year
DOI
Venue
2002
10.1109/TGRS.2002.800273
Geoscience and Remote Sensing, IEEE Transactions  
Keywords
Field
DocType
adaptive signal processing,image classification,pattern clustering,vegetation mapping,Brazilian Amazon,Landsat TM images,Landsat Thematic Mapper scenes,MPAC,Mato Grosso test site,Para test site,anthropogenic activities,closed-canopy forest,contextual clustering,deforestation mapping program,degraded forest assessment,forest alterations,image labeling,image processing,image structures,modified adaptive pappas clustering,neural network,remotely sensed images,three-stage classifier
Thematic Mapper,Vegetation,Remote sensing,Image processing,Amazon rainforest,Pixel,Deforestation,Cluster analysis,Contextual image classification,Mathematics
Journal
Volume
Issue
ISSN
40
8
0196-2892
Citations 
PageRank 
References 
3
0.50
18
Authors
4
Name
Order
Citations
PageRank
M. Sgrenzaroli191.23
A. Baraldi240135.88
eva hugh36513.38
g de grandi4128.50