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Article: Using Deep Learning for Automatic Water Stage Measurements

Eltner, Anette / Bressan, Patrik Olã / Akiyama, Thales / Gonçalves, Wesley Nunes / Marcato Junior, José

Water resources research. 2021 Mar., v. 57, no. 3

2021

Abstract: Image‐based gauging stations can allow for significant densification of monitoring networks of river water stages. However, thus far, most camera gauges do not provide the robustness of accurate measurements due to the varying appearance of water in the ... ...

Abstract	Image‐based gauging stations can allow for significant densification of monitoring networks of river water stages. However, thus far, most camera gauges do not provide the robustness of accurate measurements due to the varying appearance of water in the stream throughout the year. We introduce an approach that allows for automatic and reliable water stage measurement combining deep learning and photogrammetric techniques. First, a convolutional neural network (CNN), a class of deep learning, is applied to the segmentation (i.e., pixel classification) of water in images. The CNNs SegNet and fully convolutional network (FCN) are associated with a transfer learning strategy to segment water on images acquired by a Raspberry Pi camera. Errors of water segmentation with the two CNNs are lower than 3%. Second, the image information is transformed into metric water stage values by intersecting the extracted water contour, generated using the segmentation results, with a 3D model reconstructed with structure‐from‐motion (SfM) photogrammetry. The highest correlations between a reference gauge and the image‐based approaches reached 0.93, and average deviations were lower than 4 cm. Our approach allows for the densification of river monitoring networks based on camera gauges, providing accurate water stage measurements.
Keywords	cameras ; neural networks ; photogrammetry ; raspberries ; research ; river water ; rivers ; streams
Language	English
Dates of publication	2021-03
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	JOURNAL ARTICLE
ZDB-ID	5564-5
ISSN	1944-7973 ; 0043-1397
ISSN (online)	1944-7973
ISSN	0043-1397
DOI	10.1029/2020WR027608
Database	NAL-Catalogue (AGRICOLA)

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Article: Improving Pantanal fish species recognition through taxonomic ranks in convolutional neural networks

dos Santos, Anderson Aparecido / Gonçalves, Wesley Nunes

Ecological informatics. 2019 Sept., v. 53

2019

Abstract: Fish species recognition is an important task to preserve ecosystems, feed humans, and tourism. In particular, the Pantanal is a wetland region that harbors hundreds of species and is considered one of the most important ecosystems in the world. In this ... ...

Abstract	Fish species recognition is an important task to preserve ecosystems, feed humans, and tourism. In particular, the Pantanal is a wetland region that harbors hundreds of species and is considered one of the most important ecosystems in the world. In this paper, we present a new method based on convolutional neural networks (CNNs) for Pantanal fish species recognition. A new CNN composed of three branches that classify the fish species, family and order is proposed with the aim of improving the recognition of species with similar characteristics. The branch that classifies the fish species uses information learned from the family and order, which has shown to improve the overall accuracy. Results on unrestricted image dataset showed that the proposed method provides superior results to traditional approaches. Our method obtained an accuracy of 0.873 versus 0.864 of traditional CNN in recognition of 68 fish species. In addition, our method provides fish family and order recognition, which obtained accuracies of 0.938 and 0.96, respectively. We hope that, with these promising results, an automatic tool can be developed to monitor species in an important region such as the Pantanal.
Keywords	data collection ; ecosystems ; fish ; humans ; neural networks ; tourism ; wetlands ; Pantanal
Language	English
Dates of publication	2019-09
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	2212016-6
ISSN	1878-0512 ; 1574-9541
ISSN (online)	1878-0512
ISSN	1574-9541
DOI	10.1016/j.ecoinf.2019.100977
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: The Potential of Visual ChatGPT For Remote Sensing

Osco, Lucas Prado / de Lemos, Eduardo Lopes / Gonçalves, Wesley Nunes / Ramos, Ana Paula Marques / Junior, José Marcato

2023

Abstract: Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model ... ...

Abstract	Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT's LLM capabilities with visual computation to enable effective image analysis. The model's ability to process images based on textual inputs can revolutionize diverse fields. However, its application in the remote sensing domain remains unexplored. This is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model's limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
Subject code	004
Publishing date	2023-04-25
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A Building Roof Identification CNN Based on Interior-Edge-Adjacency Features Using Hyperspectral Imagery

Ye, Chengming / Li, Hongfu / Li, Chunming / Liu, Xin / Li, Yao / Li, Jonathan / Gonçalves, Wesley Nunes / Junior, José Marcato

Remote Sensing. 2021 July 26, v. 13, no. 15

2021

Abstract: Hyperspectral remote sensing can obtain both spatial and spectral information of ground objects. It is an important prerequisite for a hyperspectral remote sensing application to make good use of spectral and image features. Therefore, we improved the ... ...

Abstract	Hyperspectral remote sensing can obtain both spatial and spectral information of ground objects. It is an important prerequisite for a hyperspectral remote sensing application to make good use of spectral and image features. Therefore, we improved the Convolutional Neural Network (CNN) model by extracting interior-edge-adjacency features of building roof and proposed a new CNN model with a flexible structure: Building Roof Identification CNN (BRI-CNN). Our experimental results demonstrated that the BRI-CNN can not only extract interior-edge-adjacency features of building roof, but also change the weight of these different features during the training process, according to selected samples. Our approach was tested using the Indian Pines (IP) data set and our comparative study indicates that the BRI-CNN model achieves at least 0.2% higher overall accuracy than that of the capsule network model, and more than 2% than that of CNN models.
Keywords	comparative study ; data collection ; hyperspectral imagery ; neural networks
Language	English
Dates of publication	2021-0726
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13152927
Database	NAL-Catalogue (AGRICOLA)

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Article: Adversarial unsupervised domain adaptation for 3D semantic segmentation with multi-modal learning

Liu, Wei / Luo, Zhiming / Cai, Yuanzheng / Yu, Ying / Ke, Yang / Junior, José Marcato / Gonçalves, Wesley Nunes / Li, Jonathan

International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) ISPRS journal of photogrammetry and remote sensing. 2021 June, v. 176

2021

Abstract: Semantic segmentation in 3D point-clouds plays an essential role in various applications, such as autonomous driving, robot control, and mapping. In general, a segmentation model trained on one source domain suffers a severe decline in performance when ... ...

Abstract	Semantic segmentation in 3D point-clouds plays an essential role in various applications, such as autonomous driving, robot control, and mapping. In general, a segmentation model trained on one source domain suffers a severe decline in performance when applied to a different target domain due to the cross-domain discrepancy. Various Unsupervised Domain Adaptation (UDA) approaches have been proposed to tackle this issue. However, most are only for uni-modal data and do not explore how to learn from the multi-modality data containing 2D images and 3D point clouds. We propose an Adversarial Unsupervised Domain Adaptation (AUDA) based 3D semantic segmentation framework for achieving this goal. The proposed AUDA can leverage the complementary information between 2D images and 3D point clouds by cross-modal learning and adversarial learning. On the other hand, there is a highly imbalanced data distribution in real scenarios. We further develop a simple and effective threshold-moving technique during the final inference stage to mitigate this issue. Finally, we conduct experiments on three unsupervised domain adaptation scenarios, ie., Country-to-Country (USA →Singapore), Day-to-Night, and Dataset-to-Dataset (A2D2 →SemanticKITTI). The experimental results demonstrate the effectiveness of proposed method that can significantly improve segmentation performance for rare classes. Code and trained models are available at https://github.com/weiliu-ai/auda.
Keywords	decline ; models ; photogrammetry
Language	English
Dates of publication	2021-06
Size	p. 211-221.
Publishing place	Elsevier B.V.
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2021.04.012
Database	NAL-Catalogue (AGRICOLA)

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Article: Active Fire Mapping on Brazilian Pantanal Based on Deep Learning and CBERS 04A Imagery

Higa, Leandro / Marcato Junior, José / Rodrigues, Thiago / Zamboni, Pedro / Silva, Rodrigo / Almeida, Laisa / Liesenberg, Veraldo / Roque, Fábio / Libonati, Renata / Gonçalves, Wesley Nunes / Silva, Jonathan

Remote Sensing. 2022 Jan. 31, v. 14, no. 3

2022

Abstract: Fire in Brazilian Pantanal represents a serious threat to biodiversity. The Brazilian National Institute of Spatial Research (INPE) has a program named Queimadas, which estimated from January 2020 to October 2020, a burned area in Pantanal of ... ...

Abstract	Fire in Brazilian Pantanal represents a serious threat to biodiversity. The Brazilian National Institute of Spatial Research (INPE) has a program named Queimadas, which estimated from January 2020 to October 2020, a burned area in Pantanal of approximately 40,606 km2. This program also provides daily data of active fire (fires spots) from a methodology that uses MODIS (Aqua and Terra) sensor data as reference satellites, which presents limitations mainly when dealing with small active fires. Remote sensing researches on active fire dynamics have contributed to wildfire comprehension, despite generally applying low spatial resolution data. Convolutional Neural Networks (CNN) associated with high- and medium-resolution remote sensing data may provide a complementary strategy to small active fire detection. We propose an approach based on object detection methods to map active fire in the Pantanal. In this approach, a post-processing strategy is adopted based on Non-Max Suppression (NMS) to reduce the number of highly overlapped detections. Extensive experiments were conducted, generating 150 models, as five-folds were considered. We generate a public dataset with 775-RGB image patches from the Wide Field Imager (WFI) sensor onboard the China Brazil Earth Resources Satellite (CBERS) 4A. The patches resulted from 49 images acquired from May to August 2020 and present a spatial and temporal resolutions of 55 m and five days, respectively. The proposed approach uses a point (active fire) to generate squared bounding boxes. Our findings indicate that accurate results were achieved, even considering recent images from 2021, showing the generalization capability of our models to complement other researches and wildfire databases such as the current program Queimadas in detecting active fire in this complex environment. The approach may be extended and evaluated in other environmental conditions worldwide where active fire detection is still a required information in fire fighting and rescue initiatives.
Keywords	biodiversity ; data collection ; fire detection ; satellites ; wildfires ; Brazil ; China ; Pantanal
Language	English
Dates of publication	2022-0131
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14030688
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: The Segment Anything Model (SAM) for Remote Sensing Applications

Osco, Lucas Prado / Wu, Qiusheng / de Lemos, Eduardo Lopes / Gonçalves, Wesley Nunes / Ramos, Ana Paula Marques / Li, Jonathan / Junior, José Marcato

From Zero to One Shot

2023

Abstract: Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM ... ...

Abstract	Segmentation is an essential step for remote sensing image processing. This study aims to advance the application of the Segment Anything Model (SAM), an innovative image segmentation model by Meta AI, in the field of remote sensing image analysis. SAM is known for its exceptional generalization capabilities and zero-shot learning, making it a promising approach to processing aerial and orbital images from diverse geographical contexts. Our exploration involved testing SAM across multi-scale datasets using various input prompts, such as bounding boxes, individual points, and text descriptors. To enhance the model's performance, we implemented a novel automated technique that combines a text-prompt-derived general example with one-shot training. This adjustment resulted in an improvement in accuracy, underscoring SAM's potential for deployment in remote sensing imagery and reducing the need for manual annotation. Despite the limitations encountered with lower spatial resolution images, SAM exhibits promising adaptability to remote sensing data analysis. We recommend future research to enhance the model's proficiency through integration with supplementary fine-tuning techniques and other networks. Furthermore, we provide the open-source code of our modifications on online repositories, encouraging further and broader adaptations of SAM to the remote sensing domain. Comment: 20 pages, 9 figures
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-06-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: MTLSegFormer

Goncalves, Diogo Nunes / Junior, Jose Marcato / Zamboni, Pedro / Pistori, Hemerson / Li, Jonathan / Nogueira, Keiller / Goncalves, Wesley Nunes

Multi-task Learning with Transformers for Semantic Segmentation in Precision Agriculture

2023

Abstract: Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with independent branches for each task, and the exchange of information between the ... ...

Abstract	Multi-task learning has proven to be effective in improving the performance of correlated tasks. Most of the existing methods use a backbone to extract initial features with independent branches for each task, and the exchange of information between the branches usually occurs through the concatenation or sum of the feature maps of the branches. However, this type of information exchange does not directly consider the local characteristics of the image nor the level of importance or correlation between the tasks. In this paper, we propose a semantic segmentation method, MTLSegFormer, which combines multi-task learning and attention mechanisms. After the backbone feature extraction, two feature maps are learned for each task. The first map is proposed to learn features related to its task, while the second map is obtained by applying learned visual attention to locally re-weigh the feature maps of the other tasks. In this way, weights are assigned to local regions of the image of other tasks that have greater importance for the specific task. Finally, the two maps are combined and used to solve a task. We tested the performance in two challenging problems with correlated tasks and observed a significant improvement in accuracy, mainly in tasks with high dependence on the others. Comment: Accepted 4th Agriculture-Vision Workshop - CVPRW
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
Subject code	004
Publishing date	2023-05-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Supervised learning algorithms in the classification of plant populations with different degrees of kinship

Skowronski, Leandro / de Moraes, Paula Martin / de Moraes, Mario Luiz Teixeira / Gonçalves, Wesley Nunes / Constantino, Michel / Costa, Celso Soares / Fava, Wellington Santos / Costa, Reginaldo B

Revista brasileira de botânica. 2021 June, v. 44, no. 2

2021

Abstract: The population discrimination and the classification of individuals have great importance for genetic improvement in population studies and genetic diversity conservation. Furthermore, multivariate approaches are often used, especially the Fisher and ... ...

Abstract	The population discrimination and the classification of individuals have great importance for genetic improvement in population studies and genetic diversity conservation. Furthermore, multivariate approaches are often used, especially the Fisher and Anderson discriminant functions. New methodologies based on machine learning (ML) have shown to be promising for such procedures, but there is nonetheless a need for further evaluation and comparison of these methods. Thus, the present study evaluates the efficacy of supervised ML algorithms in classifying populations with different degrees of similarity—comparing them with discriminant analysis techniques proposed by Anderson and by Fisher. The methods of supervised ML tested were as follows: Naive Bayes, Decision Tree, k-Nearest Neighbors (kNN), Random Forest, Support Vector Machine (SVM) and Multi-layer Perceptron Neural Networks (MLP/ANN). To compare classification methods, we used phenotypic data of populations with different degrees of genetic similarity. Data stemmed from the genotypic information simulation for different populations submitted to the backcrossing scheme. Accuracy here means 30 repetitions from each classification method were compared by the Friedman and Nemenyi tests with a 95% confidence level. Classification methods based on machine learning algorithms showed superior results to the Fisher and Anderson discriminant functions, obtaining high accuracy where there was a higher similarity between populations. The kNN, Random Forest, SVM and Naive Bayes algorithms presented the highest accuracy, surpassing the Decision Tree algorithm and even MLP/ANN (which lost accuracy at a 96.88% similarity condition between populations). Thus, the present work confirms that ML techniques demonstrate greater accuracy in the discrimination and classification of populations without the limitations of statistical techniques.
Keywords	backcrossing ; decision support systems ; discriminant analysis ; genetic improvement ; genetic similarity ; genetic variation ; kinship ; phenotype ; support vector machines
Language	English
Dates of publication	2021-06
Size	p. 371-379.
Publishing place	Springer International Publishing
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	2686406-X
ISSN	1806-9959 ; 0100-8404
ISSN (online)	1806-9959
ISSN	0100-8404
DOI	10.1007/s40415-021-00703-1
Database	NAL-Catalogue (AGRICOLA)

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Article: Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images

Zamboni, Pedro / Junior, José Marcato / Silva, Jonathan de Andrade / Miyoshi, Gabriela Takahashi / Matsubara, Edson Takashi / Nogueira, Keiller / Gonçalves, Wesley Nunes

Remote Sensing. 2021 June 25, v. 13, no. 13

2021

Abstract: Urban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of ... ...

Abstract	Urban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications.
Keywords	climate change ; geographical distribution ; orthophotography ; population growth ; tree crown ; trees ; urban areas
Language	English
Dates of publication	2021-0625
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13132482
Database	NAL-Catalogue (AGRICOLA)

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