Towards vegetation species discrimination by using data-driven descriptors

Nogueira, Keiller; Santos, Jefersson A. dos; Fornazari, Tamires; Silva, Thiago Sanna Freire; Morellato, Leonor Patrícia Cerdeira; Torres, Ricardo da S.

doi:10.1109/prrs.2016.7867024

Cited by 12 publications

(13 citation statements)

References 33 publications

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“…on. With the accumulation of field crowdsourced samples, we believe that the workload of the manual examinations could be greatly reduced in the future by employing deep learning-based classification methods [63][64][65].…”

Section: Discussionmentioning

confidence: 99%

An updated Vegetation Map of China (1:1000000)

Guo

et al. 2020

Science Bulletin

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Section: Discussionmentioning

confidence: 99%

An updated Vegetation Map of China (1:1000000)

Guo

et al. 2020

Science Bulletin

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“…Also, if Landsat-like images (around 30 meters of spatial resolution) are employed to perform Cerrado vegetation mapping, some mixture of classes is bound to be contained in the result, regardless of the algorithm usedsee the works of Jacon et al (2017) and Girolamo Neto (2018). Nogueira et al (2016) was the only work that employed a Deep Learning-based method applied to Cerrado vegetation. They considered the same three classes that are of interest for this work, however, they performed what is called classification in computer vision, i.e., patches of Landsat images were entirely designated as Forest, Savanna or Grassland.…”

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation of Brazilian Savanna Vegetation Using High Spatial Resolution Satellite Data and U-Net

Neves¹,

Körting²,

Fonseca³

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation using remote sensing images is still a challenge due to the high spatial variability and spectral similarity of the different characteristic vegetation types (physiognomies). In this paper, we report on semantic segmentation of the three major groups of physiognomies in the Cerrado biome (Grasslands, Savannas and Forests) using a fully convolutional neural network approach. The study area, which covers a Brazilian conservation unit, was divided into three regions to enable testing the approach in regions that were not used in the training phase. A WorldView-2 image was used in cross validation experiments, in which the average overall accuracy achieved with the pixel-wise classifications was 87.0%. The F-1 score values obtained with the approach for the classes Grassland, Savanna and Forest were of 0.81, 0.90 and 0.88, respectively. Visual assessment of the semantic segmentation outcomes was also performed and confirmed the quality of the results. It was observed that the confusion among classes occurs mainly in transition areas, where there are adjacent physiognomies if a scale of increasing density is considered, which agrees with previous studies on natural vegetation mapping for the Cerrado biome.

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“…In this PhD thesis, we proposed solutions that address important challenges related to the exploitation of deep learning into the remote sensing domain, including data availability, context exploitation, and so on. It was completed in approximately four years (from March 2015 to May 2019) and has resulted in four international journal papers [5], [8], [23], [25], and eleven international conference papers [3], [6], [7], [22], [24], [27]- [32].…”

Section: Discussionmentioning

confidence: 99%

“…Based on this argument, new technologies have been proposed toward acquiring aerial images with improved quality, resulting in more advanced satellites launched to observe the Earth, as well as, more recently, in drones and unmanned aerial vehicles. These top-notch Remote Sensing Images (RSIs) may provide useful information that could be employed in several Earth Observation applications, including urban planning [1], crop and forest management [2], [3], disaster relief [4], [5], phenological studies [6]- [8], etc.…”

Section: Introductionmentioning

confidence: 99%

Going Deep into Remote Sensing Spatial Feature Learning

Nogueira¹

2020

Anais Estendidos Da Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2020)

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A lot of information may be extracted from the Earth’s surface through aerial images. This information may assist in myriad applications, such as urban planning, crop and forest management, disaster relief, etc. However, the process of distilling this information is strongly based on efficiently encoding the spatial features, a challenging task. Facing this, Deep Learning is able to learn specific data-driven features. This PhD thesis1 introduces deep learning into the remote sensing domain. Specifically, we tackled two main tasks, scene and pixel classification, using Deep Learning to encode spatial features over high-resolution remote sensing images. First, we proposed an architecture and analyze different strategies to exploit Convolutional Networks for image classification. Second, we introduced a network and proposed a new strategy to better exploit multi-context information in order to improve pixelwise classification. Finally, we proposed a new network based on morphological operations towards better learning of some relevant visual features.

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Towards vegetation species discrimination by using data-driven descriptors

Cited by 12 publications

References 33 publications

An updated Vegetation Map of China (1:1000000)

An updated Vegetation Map of China (1:1000000)

Semantic Segmentation of Brazilian Savanna Vegetation Using High Spatial Resolution Satellite Data and U-Net

Going Deep into Remote Sensing Spatial Feature Learning

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