Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

Hütt, Christoph; Koppe, Wolfgang; Miao, Yuxin; Bareth, Georg

doi:10.3390/rs8080684

Cited by 104 publications

(43 citation statements)

References 51 publications

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“…The final classification or prediction results are obtained by voting [7,54]. A large number of studies have proved that the random forest algorithm has high prediction accuracy [55,56], good tolerance for abnormal values and noise, and is not prone to over-fitting.…”

Section: Methodsmentioning

confidence: 99%

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Zhou

Pan

Zhang

et al. 2017

Sensors

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Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data.

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Section: Methodsmentioning

confidence: 99%

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Zhou

Pan

Zhang

et al. 2017

Sensors

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“…The RF classifier is an ensemble learning technique based on multiple decision trees. A large number of decision trees were generated using a resampling technique with replacement, with each tree being fitted to a different bootstrapped training sample and a randomly-selected set of predictive variables [26,40,48]. The final classification was performed through a majority vote of the trees.…”

Section: Classifiersmentioning

confidence: 99%

Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

Zhou

Zhao

Sun

et al. 2017

IJGI

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The objective of this research was to investigate the impact of seasonality on urban land-cover mapping and to explore better classification accuracy by using multi-season Sentinel-1A and GF-1 wide field view (WFV) images, and the combinations of both types of images in subtropical monsoon-climate regions in Southeast China. We obtained multi-season Sentinel-1A and GF-1 WFV images, as well as the combinations of both data, by using a support vector machine (SVM) and a random forest (RF) classifier. The backscatter intensity, texture, and interference-coherence images were extracted from Sentinel-1A images, and different combinations of these Sentinel-1A-derived images were used to evaluate their ability to map urban land cover. The results showed that the performance of winter images was better than that of any other season, while the summer images performed the worst. Higher classification accuracy was achieved by using multi-season images, and satisfactory classification results were obtained when using Sentinel-1A images from only three seasons. The best classification result was achieved using a combination of all Sentinel-1A data from all four seasons and GF-1 WFV data from winter, with an overall accuracy of up to 96.02% and a kappa coefficient reaching 0.9502. The performance of textures was slightly better than that of the backscatter-intensity images. Although the coherence data performed the worst, it was still able to distinguish urban impervious surfaces well. In addition, the overall classification accuracy of RF was better than that of SVM. and is widely used in urban mapping [4][5][6][7]. However, because optical remote sensing is susceptible to the effects of cloudy and rainy weather, accurate mapping using optical images is limited. It has been demonstrated that by using all-weather, day-and-night imaging, as well as canopy penetration and high-resolution capabilities [8-10], Synthetic Aperture Radar (SAR) images effectively overcome these limitations in land-cover classification.Earlier studies that investigated LULC information via SAR data mostly used single-frequency and single-polarization images as data sources. However, the limited information derived from single-frequency and single-polarization SAR data leads to limited classification accuracy [11]. As a result of the continuous development of radar technology, ALOS-PALSAR, Terra-SAR, and RADARSAT-2 satellites were launched; some researchers used multi-frequency and multi-polarization SAR data for urban mapping [12,13]. Tan et al. [12] reported that multi-polarization achieved better classification results than single-polarization, and that HV contributed more than the other three polarizations. Pellizzeri et al. [13] used multi-temporal/multi-band SAR data for urban mapping and obtained satisfactory classification results. There are also some studies that show how the fusion of optical and SAR images improves the classification accuracy of urban LULC [14][15][16].In addition to backscatter-intensity information, some other information c...

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“…However, many SAR LULC classification algorithms mainly focus on specific tasks which may not generalize well on other SAR tasks, for example, classifying vegetation and agricultural land covers in farmland areas [5], or training an urban-specific network with two scene images in urban areas [6]. Although a big volume of SAR data all over the world is acquired every day, it is challenging to automatically interpret land covers in SAR images with such diversity and to obtain a well-generalized model for SAR image understanding.…”

Section: Introductionmentioning

confidence: 99%

Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning

Huang¹

2020

Preprint

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The classification of large-scale high-resolution SAR land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover dataset collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100,000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover dataset (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes' problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing dataset, a robust performance on highly imbalanced classes, and is alleviating the over-fitting problem caused by label noise. What's more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%.

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Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images

Cited by 104 publications

References 51 publications

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

Exploring the Impact of Seasonality on Urban Land-Cover Mapping Using Multi-Season Sentinel-1A and GF-1 WFV Images in a Subtropical Monsoon-Climate Region

Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning

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