Mining Patterns of Change in Remote Sensing Image Databases

Computers & Geosciences

2013

Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in one year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical analysis, but up to now have had limited use in remote sensing image interpretation due to the lack of appropriate tools. The toolbox described in this paper is the Geographic Data Mining Analyst (GeoDMA). It has algorithms for segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis. GeoDMA uses decision-tree strategies adapted for spatial data mining. It connects remotely sensed imagery with other geographic data types using access to local or remote databases. GeoDMA has methods to assess the accuracy of simulation models, as well as tools for spatio-temporal analysis, including a visualization of time-series that helps users to find patterns in cyclic events. The software includes a new approach for analyzing spatio-temporal data based on polar coordinates transformation. This method creates a set of descriptive features that improves the classification accuracy of multi-temporal image databases. GeoDMA is tightly integrated with TerraView GIS, so its users have access to all traditional GIS features. To demonstrate GeoDMA, we show two case studies on land use and land cover change.

Section: Geodma Descriptionmentioning

confidence: 99%

GeoDMA—Geographic Data Mining Analyst

Körting

Computers & Geosciences

2013

“…Different actors involved in the change of the land use (small farmers, farmers, cattle breeders) can be distinguished by their different land use patterns [17]. These patterns evolve in time; new small settlements can emerge and large farms increase their agricultural area at the expense of the forest [24]. Silva (2005) treats the problem of detection of land use change patterns starting from the establishment of a land use and land cover typology and using methods for extraction of semantic information in satellite images and data mining techniques [24].…”

Section: Introductionmentioning

confidence: 99%

“…The goal of this paper is to extend the method proposed by Silva (2005) to identify cases and define how deforestation objects evolve in a rural settlement in Rondônia using of Case-Based Reasoning (CBR) technique. We presented a brief revision about CBR technique and its applications in section 2.…”

Section: Introductionmentioning

confidence: 99%

“…Silva (2005) treats the problem of detection of land use change patterns starting from the establishment of a land use and land cover typology and using methods for extraction of semantic information in satellite images and data mining techniques [24]. A structural classifier was used linking the specialist knowledge to patterns detected in the images [24]. One of the limitations of this approach is that the pattern is classified for a specific date representing a new object characterized as a new occupation.…”

Section: Introductionmentioning

confidence: 99%

“…These patterns evolve in time; new small settlements can emerge and large farms increase their agricultural area at the expense of the forest [24]. Silva (2005) treats the problem of detection of land use change patterns starting from the establishment of a land use and land cover typology and using methods for extraction of semantic information in satellite images and data mining techniques [24]. A structural classifier was used linking the specialist knowledge to patterns detected in the images [24].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Applying case-based reasoning in the evolution of deforestation patterns in the Brazilian Amazonia

Mota

Proceedings of the 2008 ACM Symposium on Applied Computing

et al. 2008

Patterns of deforestation in Brazilian Amazonia are often associated with different actors, their economic activities and their land use and land cover change strategies. These patterns are often analyzed as static landscape objects, they are not treated as objects evolving along the time. This paper proposes to apply the CaseBased Reasoning (CBR) technique to establish rules and detect object deforestation evolution in an Amazonia region. The objects were analyzed in a sequence of images from different periods creating the base and rules to apply CBR. The application of CBR can help the specialist to understand how different landscape objects evolve, to establish object trajectories and to represent that evolution.

A Geographical Approach to Self-Organizing Maps Algorithm Applied to Image Segmentation

Körting

Advanced Concepts for Intelligent Vision Systems

2011

Abstract. Image segmentation is one of the most challenging steps in image processing. Its results are used by many other tasks regarding information extraction from images. In remote sensing, segmentation generates regions according to found targets in a satellite image, like roofs, streets, trees, vegetation, agricultural crops, or deforested areas. Such regions differentiate land uses by classification algorithms. In this paper we investigate a way to perform segmentation using a strategy to classify and merge spectrally and spatially similar pixels. For this purpose we use a geographical extension of the Self-Organizing Maps (SOM) algorithm, which exploits the spatial correlation among near pixels. The neurons in the SOM will cluster the objects found in the image, and such objects will define the image segments.