Automatic target recognition method for multitemporal remote sensing image

Shu, Chang; Sun, Liankun

doi:10.1515/phys-2020-0015

Cited by 6 publications

(7 citation statements)

References 17 publications

(16 reference statements)

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“…With the continuous development of image target recognition technology, its application in remote sensing images has become increasingly common. Research on remote sensing image target recognition mainly includes the identification of ports [2], ships [3], buildings [4], electrical towers, water bodies, and vegetation [5], among others.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition

et al. 2023

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In recent years, remote sensing target recognition algorithms based on deep learning technology have gradually become mainstream in the field of remote sensing because of the great improvements that have been made in the accuracy of image target recognition through the use of deep learning. In the research of remote sensing image target recognition based on deep learning, an insufficient number of research samples is often an encountered issue; too small a number of research samples will cause the phenomenon of an overfitting of the model. To solve this problem, data augmentation techniques have also been developed along with the popularity of deep learning, and many methods have been proposed. However, to date, there is no literature aimed at expounding and summarizing the current state of the research applied to data augmentation for remote sensing object recognition, which is the purpose of this article. First, based on the essential principles of data augmentation methods, the existing methods are divided into two categories: data-based data augmentation methods and network-based data augmentation methods. Second, this paper subdivides and compares each method category to show the advantages, disadvantages, and characteristics of each method. Finally, this paper discusses the limitations of the existing methods and points out future research directions for data augmentation methods.

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Section: Introductionmentioning

confidence: 99%

“…The authors of [1][2][3][4][5] detail the application area of data augmentation; refs. [6][7][8][9] detail the application of data augmentation.…”

Section: Introductionmentioning

confidence: 99%

A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition

et al. 2023

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“…Since many different crops are grown in the vicinity of each other in large scale farming, the spectral properties of the target crop may overlap with those of the nearby crops, thereby resulting in faulty crop maps [8,9].The literature of crop mapping using multispectral remote sensing data establishes that using a single date imagery is a challenging task and yields a less accurate classification of crops. Temporal multispectral images have been effectively used for different applications of remote sensing and have delivered more accurate results for multiple studies as compared to single date images [10][11][12][13][14]. Similarly, multiple studies have developed methods based on temporal multispectral data in agricultural applications including specific crop identification [15,16].…”

Section: Introductionmentioning

confidence: 99%

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

et al. 2022

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This research work aims to study the effect of training parameter concept and sample size in the process of classification by using a fuzzy Possibilistic c-Means (PCM) approach for Pigeon Pea specific crop mapping. For specific class extraction, the “mean” of the training data is considered as a training parameter of the classification algorithm. In this study, we proposed an “Individual Sample as Mean” (ISM) approach where the individual training sample is accounted as a mean parameter for the fuzzy PCM classifier. In order to avoid the spectral overlap of target Pigeon pea crop with other crops in the study area, a temporal indices database was generated from Sentinel 2A/2B satellite images acquired during the 2019–2020 Pigeon Pea crop cycle. The spectral dimensionality of temporal data was reduced to extract the required bands to achieve maximum enhancement of the target crop class in the temporal data. Further, the training sample size was increased to study the heterogeneity within the class in the classified output. The proposed ISM approach delivered a higher mean membership difference (MMD) between the Pigeon Pea crop and the co-cultivated Cotton crop as compared to the conventional mean method. This indicated that a better separation was achieved between the target crop and the spectrally similar crop grown, that were cultivated in the same study area. When the sample size was gradually increased from 5 to 60, the MMD values within the Pigeon Pea test fields remained in the range 0.013–0.02, thereby implying that the proposed algorithm works better even with a small number of training samples. The heterogeneity was better handled using the proposed ISM approach since the variance obtained within Pigeon Pea field was only 0.008, as compared to that of 0.02 achieved using the conventional mean approach.

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“…Image segmentation technology is to divide the image into different types of uniform areas according to the internal characteristics of the image [1]. It is required that the segmentation edges between regions must be accurately delineated, and the internal features of the segmented objects have consistency or similarity.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model

Tian

Zhong

Chen

2021

Scientific Programming

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Accurate remote sensing image segmentation can guide human activities well, but current image semantic segmentation methods cannot meet the high-precision semantic recognition requirements of complex images. In order to further improve the accuracy of remote sensing image semantic segmentation, this paper proposes a new image semantic segmentation method based on Generative Adversarial Network (GAN) and Fully Convolutional Neural Network (FCN). This method constructs a deep semantic segmentation network based on FCN, which can enhance the receptive field of the model. GAN is integrated into FCN semantic segmentation network to synthesize the global image feature information and then accurately segment the complex remote sensing image. Through experiments on a variety of datasets, it can be seen that the proposed method can meet the high-efficiency requirements of complex image semantic segmentation and has good semantic segmentation capabilities.

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Automatic target recognition method for multitemporal remote sensing image

Cited by 6 publications

References 17 publications

A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition

A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

Semantic Segmentation of Remote Sensing Image Based on GAN and FCN Network Model

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