A short survey of hyperspectral remote sensing applications in agriculture

Teke, Mustafa; Deveci, Hüsne Seda; Hali̇loğlu, Onur; Gürbüz, Sevgi Zübeyde; Sakarya, Ufuk

doi:10.1109/rast.2013.6581194

Cited by 131 publications

(58 citation statements)

References 27 publications

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“…Images constitute, in many cases, a complete picture of the agricultural environments and could address a variety of challenges (Liaghat & Balasundram, 2010), (Ozdogan, Yang, Allez, & Cervantes, 2010). Hence, imaging analysis is an important research area in the agricultural domain and intelligent data analysis techniques are being used for image identification/classification, anomaly detection etc., in various agricultural applications (Teke, Deveci, Haliloğlu, Gürbüz, & Sakarya, 2013), (Saxena & Armstrong, 2014), (Singh, Ganapathysubramanian, Singh, & Sarkar, 2016). The most popular techniques and applications are presented in Appendix I, together with the sensing methods employed to acquire the images.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in agriculture: A survey

Kamilaris

Prenafeta-Boldú

2018

Computers and Electronics in Agriculture

2,402

987

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Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study.Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

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Section: Introductionmentioning

confidence: 99%

Deep learning in agriculture: A survey

Kamilaris

Prenafeta-Boldú

2018

Computers and Electronics in Agriculture

2,402

987

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“…As a result, each pixel provides a spectral signature that contains a highly detailed and unique representation of the reflectance for each captured land-cover material. This leads to a better discrimination among the different materials contained in the image, allowing hyperspectral imagery (HSI) to serve as a tool for the analysis of the surface of the Earth in many applications [26][27][28][29]. The analysis of HSIs involves a wide range of techniques, including classification [29,30], spectral unmixing [31][32][33][34], target and anomaly detection [35][36][37][38].…”

Section: Hyperspectral Image Classificationmentioning

confidence: 99%

Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

et al. 2018

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Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.

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“…Studies were mostly regarded to mineral exploration [20], but also landmine detection [21], agroforestry and related areas [22]. Back then, hyperspectral imaging technology did not have the supporting resources to go mainstream because developments in electronics, computing and software areas were required.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

et al. 2017

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Traditional imagery-provided, for example, by RGB and/or NIR sensors-has proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre-and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestry-wherein the combination of UAV and hyperspectral sensors plays a center role-is presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processing-by isolating the common user from the processes' mathematical complexity-several available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

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A short survey of hyperspectral remote sensing applications in agriculture

Cited by 131 publications

References 27 publications

Deep learning in agriculture: A survey

Deep learning in agriculture: A survey

Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry

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