Forest type classification using data fusion of multispectral and panchromatic high-resolution satellite imageries

Abstract

This paper proposes fusion analysis of high-resolution multispectral and panchromatic satellite imageries for forest type classification.We have shown the performance of forest type classification using panchromatic and multispectral high-resolution QuickBird satellite imageries separately. With texture features obtained from a panchromatic imagery, forest was classified into two types, such as coniferous and broad-leaved forests. On the other hand, with spectral features obtained from a multispectral imagery, forest was classified into six types, such as three coniferous, one broad-leaved and two mixed forests. These results showed that both texture and spectral features are effective for classification of forest types.In this paper, we apply the object-based classification using the common segments obtained from a pansharpen imagery to fusion and single imagery analysis in order to compare the difference only between texture and spectral features. The mean value of each texture and spectral feature from a segment is adopted in the supervised classification, the standard nearest neighbor method, using radiometrically corrected satellite imageries. We selected the contrast as texture feature, and normalized band values and differences between normalized band values as spectral features. From the comparison of the result with ones obtained from a single imagery analysis, we demonstrated that data fusion analysis exceeds a single imagery analysis in accuracy.

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

“…Under-or over-segmentation both decreases classification accuracy, although significant under-segmentation tends to produce much worse results than over-segmentation [9,32,33]. In this study, we adopted the Bhattacharyya Distance (BD) index method presented by Xun et al [25] and Wang et al [26] in order to determine the best segmentation scale parameter. In statistics, BD is used to measure the similarity between two discrete or continuous probability distributions and the amount of overlap between two statistical samples.…”

“…It has also proved that this method is spectrally stronger than other sharpening techniques for the fusion of WV2 multispectral bands with the panchromatic band [24]. The GSPS method has been widely applied in land cover classification [25], forest tree species classification [26], urban tree species classification [12], and detection of mineral alteration in a marly limestone formation [27]. In our study, the pan-sharpened image provides clear boundaries of tree crowns, and thus was used for further processing including segmentation and classification.…”

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

“…Very high resolution (VHR) remotely sensed imagery like QuickBird, IKONOS, and SPOT5 have proven valuable in discriminating various forest species over older generation sensors [1,2], as they can accurately describe the complex spatial patterns typically observed in forested areas. Moreover, a number of recent studies have shown that hyperspectral imagery are particularly useful for discriminating different species of the same genus, which is not always possible using the limited spectral information provided by VHR multispectral images [3][4][5][6][7][8].…”

Some Aspects of Satellite Imagery Integration From Eros B and Landsat 8

“…Coloured images with enhanced spatial resolution (ie. sharpened images) can be used to support geographic information systems (Mori, 2002), creation of land cover maps (Ochi, 2008), monitoring changes in ecosystems (Haruyama et al, 2010) and forest monitoring (cultivation planning and management, monitoring the health of trees) (Kosaka et al, 2005). …”