Progress in remote sensing (1972–1976)

Fischer, William A.; Hemphill, W. R.; Kover, Allan N.

doi:10.1016/0031-8663(76)90013-2

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…Remote sensing can be defined as "the art of science of telling something about an object without touching it" [42] or "the observation of a target by a device separated from it by some distance" [11]. It can be classified as passive or active based on the sensor; the former gathers the radiation that is incident on the sensor used (i.e., no radiation is transmitted), while the latter gathers the reflected radiation to the sensor after transmitting it to the targeted object [4], [5].…”

Section: Background and Related Workmentioning

confidence: 99%

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Grieve

AlSuwaidi

2018

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

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Abstract-Recently, there has been a significant increase in the use of proximal or remote hyperspectral imaging systems to study plant properties, types, and conditions. Numerous financial and environmental benefits of using such systems have been the driving force behind this growth. This paper is concerned with the analysis of hyperspectral data for detecting plant diseases and stress conditions and classifying crop types by means of advanced machine learning techniques. Main contribution of the work lies in the use of an innovative classification framework for the analysis, in which adaptive feature selection, novelty detection, and ensemble learning are integrated. Three hyperspectral datasets and a nonimaging hyperspectral dataset were used in the evaluation of the proposed framework. Experimental results show significant improvements achieved by the proposed method compared to the use of empirical spectral indices and existing classification methods.Index Terms-Ensemble learning, feature selection, hyperspectral imaging (HSI), novelty detection (ND), plant monitoring, remote sensing, support vector machine (SVM).

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Section: Background and Related Workmentioning

confidence: 99%

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Grieve

AlSuwaidi

2018

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

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“…Remote sensing is the science and the art of acquiring information about an object by observing it from a distance [54]. Sensors can acquire data remotely while being on board different platforms, such as satellites, aeroplanes, UAVs or handheld devices [52].…”

Section: Estimation Of Crop Parameters From Remote Sensingmentioning

confidence: 99%

Contribution of Remote Sensing on Crop Models: A Review

et al. 2018

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Abstract:Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.

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“…Therefore, surveillance is in depth research in which any factor leading to misunderstanding must be taken into account [8], used satellite data to identify and monitor various types of environmental changes such as urban development or urban fringe development, forest change, deforestation, coastalmodification, change land use in agriculture. Some research on remote sensing applied to arid zones [10][11][13] [14], are particularly noteworthy Those researchs has both concerned the monitoring of main phenomena such as surface albedo mutation, surface albedo desertification, land-use change, ecosystem degradation, and modelling of human-environment interaction, they were based on treatment results of remote sensing. One of the limitations of such monitoring LU/LCC approach is the fact that the priority is not given to determine the influential factors.…”

Section: Related Workmentioning

confidence: 99%

A new architecture for monitoring land use and land cover change based on remote sensing and GIS: A data mining approach

Oubrahim

Lbazri

Ounacer

et al. 2018

PEN

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The issue of land use (LU) and land cover change (LCC) has become crucial around the world in recent years, not only for researchers, but also for urban planners and environmentalists who advocate sustainable land use in the future. In Morocco, this phenomenon affects large areas and is all the more pronounced because the climate is arid with cycles of increasing drought and soils are poor and highly vulnerable to erosion. In addition, the precarious living conditions of rural populations pushes them to over exploit natural resources to meet their growing needs, which further amplifies environmental degradation. In this LU/LCC monitoring context, this paper aims on one hand at giving a clear survey of classical methods and techniques used to monitor LU/LCC, on other hand the authors propose a new architecture whose objective is to integer data mining techniques to the LU/LCC monitoring in order to automatically and efficiently improve the monitoring, control and asset management in LU/LCC..

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Progress in remote sensing (1972–1976)

Cited by 18 publications

References 25 publications

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Contribution of Remote Sensing on Crop Models: A Review

A new architecture for monitoring land use and land cover change based on remote sensing and GIS: A data mining approach

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