The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning

Abstract

Precision viticulture benefits from the accurate detection of vineyard vegetation from remote sensing, without a priori knowledge of vine locations. Vineyard detection enables efficient, and potentially automated, derivation of spatial measures such as length and area of crop, and hence required volumes of water, fertilizer, and other resources. Machine learning techniques have provided significant advancements in recent years in the areas of image segmentation, classification, and object detection, with neural networks shown to perform well in the detection of vineyards and other crops. However, what has not been extensively quantitatively examined is the extent to which the initial choice of input imagery impacts detection/segmentation accuracy. Here, we use a standard deep convolutional neural network (CNN) to detect and segment vineyards across Australia using DigitalGlobe Worldview-2 images at ∼50 cm (panchromatic) and ∼2 m (multispectral) spatial resolution. A quantitative assessment of the variation in model performance with input parameters during model training is presented from a remote sensing perspective, with combinations of panchromatic, multispectral, pan-sharpened multispectral, and the spectral Normalised Difference Vegetation Index (NDVI) considered. The impact of image acquisition parameters—namely, the off-nadir angle and solar elevation angle—on the quality of pan-sharpening is also assessed. The results are synthesised into a ‘recipe’ for optimising the accuracy of vineyard segmentation, which can provide a guide to others aiming to implement or improve automated crop detection and classification.

Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve “Kózki”, Poland

“…Therefore, a discussion of indicator value results cannot be conducted with respect to other studies. Our segmentation accuracy results, meanwhile, do correspond with similar studies [100,101], although it is often a challenge to compare segmentation accuracies among GEOBIA case studies due to unique methods, remote sensing imagery, and landscape types.…”

Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images

“…After the bands were stacked, general pansharpening using panchromatic images was conducted using the CS-based GSA technique and the MRA-based GS2 technique. For the GSA technique, multiple regression analysis was applied to a panchromatic image that was transformed into the number of pixels of the VNIR image and the VNIR bands to calculate the regression coefficient to generate the optimal low-spatial-resolution image I L using Equations (13) and (14):…”

“…The high-spatial-resolution multispectral images produced through pansharpening techniques distort the spatial and spectral information of the original image. This distortion occurs because the bands of the panchromatic images and those of the multispectral images have different spectral characteristics; the resulting sharpened images with distorted spectral information are difficult to utilize in analyses that rely on spectral characteristics, such as land cover mapping [14]. Recently, images with more than three spatial resolutions or images of spectral wavelengths outside the visible and near-infrared (VNIR) spectral range can be acquired in contrast to only high-spatial-resolution satellite images consisting of panchromatic and multispectral images.…”

The role of image interpolation in pansharpening

“…Possible reasons include unfavorable weather conditions, financial constraints, or intrinsic sensor limitations that prevent the acquisition of images with high color and spatial detail fidelity at the same time 1,2 . An extensive literature review revealed that the pansharpening has been resorted for a wide range of purposes, including landslide monitoring, 3 change detection, 4 mapping of diseased trees, 5 analyzing agricultural landscapes, 6 crop type differentiation, 7 vegetation mapping, 8 soil mapping, 9 coastline extraction, 10 vineyard segmentation, 11 and so forth.…”