Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data

Abstract

Recent satellite missions have provided new perspectives by offering high spatial resolution, a variety of spectral properties, and fast revisit rates to the same regions. In this study, we examined the utility of both broadband red-edge spectral information and texture features for classifying paddy rice crops in South Korea into three different growth stages. The rice grown in South Korea can be grouped into earlymaturing, medium-maturing, and medium-late-maturing cultivars, and each cultivar is known to have a minimum and maximum productivity. Therefore, the accurate classification of paddy rice crops into a certain time line enables pre-estimation of the expected rice yields. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the paddy rice crops, particularly when single-season image data were used. In contrast, texture information resulted in only minor improvement or even a slight decline in accuracy, although it is known to be advantageous for object-based classification. This was due to the homogeneous nature of paddy rice fields, as different rice cultivars are similar in terms of their morphology. Based on these results, we conclude that the additional spectral information such as the red-edge band is more useful than the texture features to detect different crop conditions in relatively homogeneous rice paddy environments. We therefore confirm the potential of broadband red-edge information to improve the classification of paddy rice crops. However, there is still a need to examine the relationship between textural properties and paddy rice crop parameters in greater depth.

Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

“…Each satellite has an equally calibrated identical pushbroom sensor and provides high-resolution multispectral imagery (nadir ground sample distance: 6.5 m; orthorectified resampled pixel size: 5 m) in five optical bands corresponding to the blue visible (440-510 nm), green visible (520-590 nm), red visible (630-690 nm), red-edge (690-730 nm), and near infrared (760-880 nm) portions of the electromagnetic spectrum. The red-edge band, which is sensitive to changes in chlorophyll content [40,41], is very useful for classifying vegetation types [42][43][44]. We used a RapidEye image collected on 3 December 2010.…”

Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

“…Ground truth data for each site were collected at the tillering stage (28-30 June), the jointing stage (10-12 July), the heading stage (2-8 August), 20 days after heading (22)(23)(24)(25)(26)(27)(28), and the harvest time. The FS-2 images were acquired on 24 June, 6 July and 9 August, at slightly different dates from the field campaigns.…”

“…RS data with coarse and medium resolution are widely used in rice cultivation research [12,[15][16][17][18][19][20][21][22][23][24][25][26]. However, the number of conducted studies on rice using high resolution RS images was limited in the past two decades [23,27,28]. Identification of rice cultivation areas and estimation of agronomic parameters from high resolution images are valuable for improving rice production.…”

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

“…Firstly, some sensors not only feature increased spatial resolution but also the number of spectral bands, such as RapidEye, Worldview-3, and WorldView-4. The red-edge band of RapidEye satellite imagery can be used to identify the type and growth state of vegetation [53,[82][83][84], which may be useful for fine-scale LCCMA. Some studies have also assessed the effect of the red-edge band on some special classifications [53,82,84].…”

“…The red-edge band of RapidEye satellite imagery can be used to identify the type and growth state of vegetation [53,[82][83][84], which may be useful for fine-scale LCCMA. Some studies have also assessed the effect of the red-edge band on some special classifications [53,82,84]. WorldView-3 (WV-3) is a newly launched (August 2014) commercial satellite sensor with high spatial resolution, eight visible to near-infrared bands (0.42 to 1.04 µm), and eight shortwave infrared (SWIR) bands (1.2 to 2.33 µm) [85].…”