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Poster Session P
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Ground target recognition: The operationally of Remote Sensing techniques in the tropics
Mazlan Hashim,Mohd Ibrahim,Smasudin Ahmad,Sarudin Awang
Center for Remote Sensing, Faculty of Surveying
University Teknologu Malaysia, Locked Bag 791
80990 Johor Bahru, Malaysia
Abstract
The successful extraction of information from satellite remotely sensed data are largely influenced by two dominant factors i.e. the atmospheric interferences inherent in the data and the target’s own spectral response behavior. The atmospheric effects can be compensated by using suitable atmospheric model. The target’s spectral signatures on the other hand are unique which enable their recognition. In reality, the target’s recognition from remotely sensed data is not trivial particularly in humid tropic regions.
This paper addresses the manipulation of digital data from the Landsat-5 TM and SPOT-1 multispectral data in order to obtain optimum classification accuracy for some selected land use categories. Summary of targets accuracies on classification of the manipulated and original data sets are tabulated. The results show that manipulation by spatial filtering and principal component analysis on the data sets have improved the classification accuracy of 6 land use categories by as much as 45 percent.
Introduction
Recognition of different land use classes from Remote Sensing data poses significant problem in humid tropic regions. This problem is mainly due to the minimum heterogeneity of the classes which will result in poor identification of classes to which they belong in the classification process. Some prominent factors discussed by Schowengerdt (1983) that cause variability within the classes are atmospheric scattering, topography, sun and view angles, class mixture, and within-class reflectance variability.
Several processing techniques attempted by Hashim and Ahmad (1989) proved that best seprabilities among land use classes can be optimized using vegetation indices, statistical filtering and merging of data from different sources.
Paper presented at the 13th Asian Conference on Remote Sensing. 7 – 11 October 1992, Mongolia
This paper will further analyze the technique of statistical filtering of the data and utilize the transformed data together with the original data as input in the principal component analysis (PCA). The PCA method was utilized to reduce the number of images that are needed for classification.
Study Area and Data Acquisition
The study area is centered on the small town of Bedong, Kedah Peninsular Malaysia with approximately 80 square kilometers (10 km X 8 km) of various land cover types. The primary land cover is rubber, oil palm, forest, mangrove and a significant portion of mining area in the western part of Bedong. Water features include rivers and disused mining pools. The North and South Highway passes through the center of the study area with other transportation network that includes railways, two-way roads and tracks.
The data used for the identification of land use and cover types were the 1985 Ministry of Agriculture Land Use Map and 1988 Topographical Maps at a Scale of 1:50000.
The satellite data used in the study were the Landsat -5 TM data of 12 February 1991 (WRS 128/56) on bands 1 to 5 and 7, and the SPOT-1 MLA data of 6 August 1988(K/J 266/339).
Several land use classes based on the national legend (Wong, 1974) were adopted for the classification process in the study area which are indicated in Table 1. Water features including rivers and mining pools were also included in the legend since they feature prominently in the image.
Data Processing
The satellite data were geometrically corrected before spatial filtering and principal component analysis were carried out.
1 Spatial Filtering
The contribution of spatial filters in the processing, before any classification takes place reduce the internal variation within the land use categories and improve the texture properties of the image data (Cushnie and Atkinson 1985). In this study, a 3 x 3 moving window filter with the returning values of standard deviation, mean and variance of the original image data to its center pixels were employed. The transformation resulted in 3 new data files for each TM bands to give a total of 18 data files. A total of 9 data files were created for SPOT data.
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