Classification of Satellite Imagery for Identifying Land-Cover Objects using ECW Compression Images: The Case of Makassar City Area

Abstract

This paper presents a case study on the effect of lossy compression using the Enhanced Compressed Wavelet (ECW) format on remote sensing image classification. ECW was chosen because it is widely used as a standard format for storing aerial and satellite imagery. The case study was conducted on a high-resolution multispectral Pleiades image taken from an area in Makassar, Indonesia. Image classification is performed using the geographic object-based image analysis method, where a simple linear iterative clustering (SLIC) algorithm is implemented for segmentation before classification. Six land cover categories were selected to validate the classification results: water bodies, trees, rice fields, shrubs, and urban areas. The effect of image compression on classification accuracy is studied by varying the compression ratio. Then the results are compared with the original image. Experimental results prove that compression with ECW format does not have much effect on classification accuracy. Even the Random Forest and Gradient Boosting Machine provide higher accuracy with the compressed image compared to the original image. In addition, it can be concluded that Random Forest is the best classifier with the highest accuracy.