Parcel Level Staple Crop Type Identification from Time Series Remote Sensing Images Based on Newly Defined Red-Edge Vegetation Indices And Ornn

Li, Xu; Ming, Dongping; Zhang, Liang; Dong, Dehui; Qing, Yuanzhao; Yang, Jianyu

doi:10.2139/ssrn.4314551

Cited by 2 publications

(3 citation statements)

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“…HODet achieves good results by using various primary object detection networks based on HBBs. The mAP reaches 87.54% when using Faster R-CNN-FPN, and it is 1.65% higher than R 3…”

Section: Experiments Results 1) Results On Hrsc2016mentioning

confidence: 96%

“…ECENTLY, deep learning technology has emerged endlessly in the field of remote sensing [1][2][3] and the research of object detection in natural scenes has developed rapidly. A series of excellent methods have been proposed for object detection, such as R-CNN [4], YOLO [5], SSD [6], FPN [7], EfficientDet [8] and other improved methods, all of which are performed well above in VOC [9] and MS COCO [10] dataset.…”

Section: Introductionmentioning

confidence: 99%

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HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

Xu,

Yu,

Ming

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Object detection from remote sensing images is a key technology for earth observation applications, which has important scientific research value. Ground objects in remote sensing images appear at arbitrary angles. However, object detection based on horizontal bounding boxes (HBBs) would cause mutual coverage among targets while the ground objects were dense-distributed or with a large aspect ratio. The oriented object detection methods could solve the problem by predicting the rotation angle. But the currently used methods were timeconsuming in labelling and require complex loss functions of networks. Thus, this paper combined HBB and oriented object detection to achieve rectangular object detection. Firstly, object detection network with HBBs is trained and the model predicts results in original remote sensing image. Secondly, the rotation angles are derived from the line detection with linear Hough transform on the cropped region of targets obtained by the first step. Then, the original image is rotated according to the rotation angles and detects object with HBBs again. Finally, new bounding boxes are mapped to the original image to get the detection results with oriented bounding boxes (OBBs).Experiments on public remote sensing datasets demonstrate that the proposed method is highly flexible and can be combined with any HBBs object detection network. The idea of original image rotation does not use the OBB labels to retrain the network, which reduces the workload of object annotations. Additionally, the method can be used to automatically generate OBBs to label rectangular objects since the public datasets commonly annotated by HBBs.

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“…HODet achieves good results by using various primary object detection networks based on HBBs. The mAP reaches 87.54% when using Faster R-CNN-FPN, and it is 1.65% higher than R 3…”

Section: Experiments Results 1) Results On Hrsc2016mentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

Xu,

Yu,

Ming

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Texture is dependent on three variables: (i) size of the area being investigated/processed; (ii) the relative sizes of the discrete tonal features; and (iii) spatial distribution of discrete tonal features [2]. Gray level co-occurrence matrix (GLCM) has proven to be a powerful basis for use in texture classification [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Radar Remote Sensing for Crop Classification

Sahu¹,

Sahu²

2023

IJPSS

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This Study proposes the approach for crop classification using the Grey Level Co-occurrence Matrix feature of Synthetic Aperture Radar (SAR) images. The method utilizes the SAR Images acquired by Sentinel 1A SAR Data and extract textural features using GLCM. In this study, we investigate the potential of Grey Level Co-occurrence Matrix (GLCM)-based texture information for horticulture crop classification with SAR images in Kharif and cloud weather condition. A study on Synthetic Aperture Radar (SAR) satellite imagery was conducted in Chhattisgarh with the objective to evaluate the potential of different texture parameters among crop. The SAR data were pre-processed for textural analysis having entire angle and equal distance quantization. The results were categorized among different parameters showing significant variation for horticulture crops for Contrast, Dissimilarity, Homogeneity, ASM, Energy, Entropy and GLCM Mean. The statistical analysis was done for fruit crop along with major kharif crop of study area. The results shows that mean backscatter value was lowest for banana (99.12 dB) and highest for Mango (198.26 dB) regarding contrast textural property in VH Channel whereas mean backscatter value in VH Channel w.r.t to energy was maximum for banana (0.60 dB) followed by papaya (0.49 dB) and guava (0.45 dB) and least for mango (0.44 dB). The mean backscatter value for GLCM mean textural property in VH channel was shown maximum by banana (51.24 dB) followed by papaya (41.96 dB) and mango (32.98 dB). These results indicate the usefulness of texture information for classification of SAR images, particularly when acquisition of optical images is difficult in Kharif and cloud weather condition for crop classification. Thus GLCM feature of SAR Data proven to be significant for the classification of horticulture crops.

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Parcel Level Staple Crop Type Identification from Time Series Remote Sensing Images Based on Newly Defined Red-Edge Vegetation Indices And Ornn

Cited by 2 publications

References 0 publications

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

HODet: A New Detector for Arbitrary-Oriented Rectangular Object in Optical Remote Sensing Imagery

Synthetic Aperture Radar Remote Sensing for Crop Classification

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