A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees

2014

IJGI

This study evaluated the effects of image pansharpening on Vegetation Indices (VIs), and found that pansharpening was able to downscale single-date and multi-temporal Landsat 8 VI data without introducing significant distortions in VI values. Four fast pansharpening methods-Fast Intensity-Hue-Saturation (FIHS), Brovey Transform (BT), Additive Wavelet Transform (AWT), and Smoothing Filter-based Intensity Modulation (SFIM)-and two VIs-Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR)-were tested. The NDVI and SR formulas were both found to cause some spatial information loss in the pansharpened multispectral (MS) bands, and this spatial information loss from VI transformations was not specific to Landsat 8 imagery (it will occur for any type of imagery). BT, SFIM, and other similar pansharpening methods that inject spatial information from the panchromatic (Pan) band by multiplication, lose all of the injected spatial information after the VI calculations. FIHS, AWT, and other similar pansharpening methods that inject spatial information by addition, lose some spatial information from the Pan band after VI calculations as well. Nevertheless, for all of the single-and multi-date VI images, the FIHS and AWT pansharpened images were more similar to the higher resolution reference data than the unsharpened VI images were, indicating that pansharpening was effective in downscaling the VI data. FIHS best enhanced the spectral and spatial information of the single-date and multi-date VI images, followed by AWT, and neither significantly over-or under-estimated VI values.

Section: Introductionmentioning

confidence: 99%

Effects of Pansharpening on Vegetation Indices

2014

IJGI

“…A ROI-based cross-validation approach would also reduce spatial autocorrelation between training and validation samples caused by their close proximity to one another, which also compromises the assumption of training/validation data set independence even for the higher resolution images [18]. It should be noted that, although Sun and Schulz simply used the resampled lower resolution TIR pixels for classification, meaning that the TIR images were not "sharpened" using the higher resolution imagery (as in some other studies on classification-oriented image fusion [5,6,9,[11][12][13][14][15]), the scale issues pointed out here also apply when the lower resolution imagery is "sharpened" using the higher resolution imagery prior to classification, as the spatial resolution of the lower resolution image is only artificially increased, and its pixel values are still derived in part from the original lower resolution image.…”

mentioning

confidence: 99%

“…Fusion of multi-resolution and/or multi-sensor remote sensing (RS) imagery has been shown to result in higher classification accuracy in many past studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15], so classification-oriented image fusion is an important research topic. Satellite data from the Landsat series is commonly-used for land use/land cover (LULC) classification in RS, and Landsat 4/5/7/8 have image bands that vary in terms of spatial resolution, as detailed in [16].…”

mentioning

confidence: 99%

Scale Issues Related to the Accuracy Assessment of Land Use/Land Cover Maps Produced Using Multi-Resolution Data: Comments on “The Improvement of Land Cover Classification by Thermal Remote Sensing”. Remote Sens. 2015, 7(7), 8368–8390

2015

Remote Sensing

Much remote sensing (RS) research focuses on fusing, i.e., combining, multi-resolution/multi-sensor imagery for land use/land cover (LULC) classification. In relation to this topic, Sun and Schulz [1] recently found that a combination of visible-to-near infrared (VNIR; 30 m spatial resolution) and thermal infrared (TIR; 100-120 m spatial resolution) Landsat data led to more accurate LULC classification. They also found that using multi-temporal TIR data alone for classification resulted in comparable (and in some cases higher) classification accuracies to the use of multi-temporal VNIR data, which contrasts with the findings of other recent research [2]. This discrepancy, and the generally very high LULC accuracies achieved by Sun and Schulz (up to 99.2% overall accuracy for a combined VNIR/TIR classification result), can likely be explained by their use of an accuracy assessment procedure which does not take into account the multi-resolution nature of the data. Sun and Schulz used 10-fold cross-validation for accuracy assessment, which is not necessarily inappropriate for RS accuracy assessment in general. However, here it is shown that the typical pixel-based cross-validation approach results in non-independent training and validation data sets when the lower spatial resolution TIR images are used for classification, which causes classification accuracy to be overestimated.

“…This often occurs if a higher spatial resolution (HSR) image is segmented and then the segment polygons are overlaid onto a lower spatial resolution (LSR) image to derive additional descriptors of the segments, such as higher spectral resolution [10,11] or higher temporal resolution information. Compared to pixel-based image fusion, which is relatively common nowadays in remote sensing [12], few studies have investigated fusion at the segment (polygon) level [10,13,14]. Here, for simplicity this procedure is referred to as segment-level fusion (also referred to as pixel/feature-level fusion in [11]).…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Fusion at the Segment Level Using a Spatially-Weighted Approach: Applications for Land Cover Spectral Analysis and Mapping

2015

IJGI

Segment-level image fusion involves segmenting a higher spatial resolution (HSR) image to derive boundaries of land cover objects, and then extracting additional descriptors of image segments (polygons) from a lower spatial resolution (LSR) image. In past research, an unweighted segment-level fusion (USF) approach, which extracts information from a resampled LSR image, resulted in more accurate land cover classification than the use of HSR imagery alone. However, simply fusing the LSR image with segment polygons may lead to significant errors due to the high level of noise in pixels along the segment boundaries (i.e., pixels containing multiple land cover types). To mitigate this, a spatially-weighted segment-level fusion (SWSF) method was proposed for extracting descriptors (mean spectral values) of segments from LSR images. SWSF reduces the weights of LSR pixels located on or near segment boundaries to reduce errors in the fusion process. Compared to the USF approach, SWSF extracted more accurate spectral properties of land cover objects when the ratio of the LSR image resolution to the HSR image resolution was greater than 2:1, and SWSF was also shown to increase classification accuracy. SWSF can be used to fuse any type of imagery at the segment level since it is insensitive to spectral differences between the LSR and HSR images (e.g., different spectral ranges of the images or different image acquisition dates).