Dr. Pengyu Hao Profile

Dr. Pengyu Hao

at Institute of Remote sensing and Digital Earth

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SPIE Journal Paper | 26 June 2015

Potential of multitemporal Gaofen-1 panchromatic/multispectral images for crop classification: case study in Xinjiang Uygur Autonomous Region, China

Pengyu Hao, Li Wang, Zheng Niu

JARS, Vol. 9, Issue 01, 096035, (June 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096035

KEYWORDS: Image segmentation, Image classification, Phase modulation, Vegetation, Near infrared, Image resolution, Spatial resolution, Visualization, Feature extraction, Reflectivity

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Gaofen-1 panchromatic/multispectral (GF-1 PMS) data have both high spatial and temporal resolutions, and this research aims at evaluating the potential of GF-1 PMS data for crop classification. Three PMS images (at days 110, 192, and 274) were acquired in Manas County of Xinjiang. The images were first segmented and all objects were then visually interpreted based on ground reference data. Some indices and textual features were then extracted at the object level. Subsequently, the Jeffries–Matusita (JM) distance was employed to estimate the class separability among all pair-wise comparisons of each time period. Afterward, a random forest algorithm was used to calculate importance scores of all features and classify crop types for every possible image combination. Additionally, to evaluate the influence of feature number on classification accuracy, features were added one by one based on the importance of scores. The result showed that GF-1 PMS images with high-spatial resolution had the potential to identify the boundary of the crop fields. Relatively high JM distance (above 1.5) and classification accuracy (above 90%) indicated that day 192 image contributed the most to the crop identification in the study area. For multi-image combinations, days 110 to 192 combination can achieve high overall accuracy (around 93%) and more images cannot substantially improve the classification performance. As for features, normalized difference vegetation index and near infrared (NIR) band had the highest importance scores and textual features contributed to distinguishing tree from crop land. Finally, classification accuracy increased together with the augmentation of feature number when only a few features were used. After accuracies reached saturation points, however, more features only slightly improved the classification performance.

Proceedings Article | 8 November 2014 Paper

Crop classification using multi-temporal HJ satellite images: case study in Kashgar, Xinjiang

Pengyu Hao, Zheng Niu, Li Wang

Proceedings Volume 9260, 926005 (2014) https://doi.org/10.1117/12.2068714

KEYWORDS: Image classification, Satellites, Charge-coupled devices, Spatial resolution, Remote sensing, Library classification systems, Satellite imaging, Earth observing sensors, Agriculture, Data acquisition

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The HJ satellite constellation, characterized as high temporal resolution (4 day revisit frequency), has high potential to obtain cloud-free images covering all cruel periods for crop classification during growing season. In this paper, three HJ images (in May, July and September) were acquired, the performances of different multi-spectral HJ CCD data combinations for crop classification in Kashgar, Xinjiang were estimated using library for Support Vector Machine (LIBSVM), and ground reference data obtained in 2011 field work were used as training and validation samples. The result showed that multi-temporal HJ data has a potential to classify crops with an overall classification accuracy of 93.77%. Among the three time periods utilized in this research, the image acquired in July achieved the highest overall accuracy (86.98%) because all summer crops were under dense canopy closure. Cotton could be accurately extracted in May image (both user and produce accuracy are above 90%) because of its lower canopy closure compared with spring, the rotate crop (wheat_maize) and winter crop (wheat) at the time period. Then, the July and September combination performed as good as that of all threetime- period combination, which indicated that images obtained at cruel time periods are enough to identify crops, and the additional images improve little on classification accuracy. In addition, multi-temporal NDVI in cruel time periods of the growing season is testified efficient to classify crops with significant phenonlogical variances since they achieved similar overall accuracy to that of multi-temporal multi-spectral combination.

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