Deep-learning feature descriptor for tree bark re-identification

L’habilité de visuellement ré-identifier des objets est une capacité fondamentale des systèmes de vision. Souvent, ces systèmes s’appuient sur une collection de signatures visuelles basées sur des descripteurs comme SIFT ou SURF. Cependant, ces descripteurs traditionnels ont été conçus pour un certain domaine d’aspects et de géométries de surface (relief limité). Par conséquent, les surfaces très texturées telles que l’écorce des arbres leur posent un défi. Alors, cela rend plus difficile l’utilisation des arbres comme points de repère identifiables à des fins de navigation (robotique) ou le suivi du bois abattu le long d’une chaîne logistique (logistique). Nous proposons donc d’utiliser des descripteurs basés sur les données, qui une fois entraîné avec des images d’écorce, permettront la ré-identification de surfaces d’arbres. À cet effet, nous avons collecté un grand ensemble de données contenant 2 400 images d’écorce présentant de forts changements d’éclairage, annotées par surface et avec la possibilité d’être alignées au pixels près. Nous avons utilisé cet ensemble de données pour échantillonner parmis plus de 2 millions de parcelle d’image de 64x64 pixels afin d’entraîner nos nouveaux descripteurs locaux DeepBark et SqueezeBark. Notre méthode DeepBark a montré un net avantage par rapport aux descripteurs fabriqués à la main SIFT et SURF. Par exemple, nous avons démontré que DeepBark peut atteindre une mAP de 87.2% lorsqu’il doit retrouver 11 images d’écorce pertinentes, i.e correspondant à la même surface physique, à une image requête parmis 7,900 images. Notre travail suggère donc qu’il est possible de ré-identifier la surfaces des arbres dans un contexte difficile, tout en rendant public un nouvel ensemble de données.The ability to visually re-identify objects is a fundamental capability in vision systems. Oftentimes,it relies on collections of visual signatures based on descriptors, such as SIFT orSURF. However, these traditional descriptors were designed for a certain domain of surface appearances and geometries (limited relief). Consequently, highly-textured surfaces such as tree bark pose a challenge to them. In turn, this makes it more difficult to use trees as identifiable landmarks for navigational purposes (robotics) or to track felled lumber along a supply chain (logistics). We thus propose to use data-driven descriptors trained on bark images for tree surface re-identification. To this effect, we collected a large dataset containing 2,400 bark images with strong illumination changes, annotated by surface and with the ability to pixel align them. We used this dataset to sample from more than 2 million 64 64 pixel patches to train our novel local descriptors DeepBark and SqueezeBark. Our DeepBark method has shown a clear advantage against the hand-crafted descriptors SIFT and SURF. For instance, we demonstrated that DeepBark can reach a mAP of 87.2% when retrieving 11 relevant barkimages, i.e. corresponding to the same physical surface, to a bark query against 7,900 images. ur work thus suggests that re-identifying tree surfaces in a challenging illuminations contextis possible. We also make public our dataset, which can be used to benchmark surfacere-identification techniques

Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery

Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.Comment: G. Sumbul, R. G. Cinbis, S. Aksoy, "Fine-Grained Object Recognition and Zero-Shot Learning in Remote Sensing Imagery", IEEE Transactions on Geoscience and Remote Sensing (TGRS), in press, 201

Forest canopy mortality during the 2018-2020 summer drought years in Central Europe: The application of a deep learning approach on aerial images across Luxembourg

Efficient monitoring of tree canopy mortality requires data that cover large areas and capture changes over time while being precise enough to detect changes at the canopy level. In the development of automated approaches, aerial images represent an under-exploited scale between high-resolution drone images and satellite data. Our aim herein was to use a deep learning model to automatically detect canopy mortality from high-resolution aerial images after severe drought events in the summers 2018–2020 in Luxembourg. We analysed canopy mortality for the years 2017–2020 using the EfficientUNet++, a state-of-the-art convolutional neural network. Training data were acquired for the years 2017 and 2019 only, in order to test the robustness of the model for years with no reference data. We found a severe increase in canopy mortality from 0.64 km2 in 2017 to 7.49 km2 in 2020, with conifers being affected at a much higher rate than broadleaf trees. The model was able to classify canopy mortality with an F1-score of 66%–71% and we found that for years without training data, we were able to transfer the model trained on other years to predict canopy mortality, if illumination conditions did not deviate severely. We conclude that aerial images hold much potential for automated regular monitoring of canopy mortality over large areas at canopy level when analysed with deep learning approaches. We consider the suggested approach a cost-efficient and -effective alternative to drone and field-based sampling

SOFTWARE FOR FOREST SPECIES RECOGNITION BASED ON DIGITAL IMAGES OF WOOD

Classifying forest species is an essential process for the correct management of wood and forest control. After cutting off the trunk of the tree, many of the characteristics of the species are lost and identifying them becomes a much more difficult task. In this context, an anatomical analysis of the wood becomes necessary, needing to be done by specialists who know very well the cellular structures of each species. However, such methodology approaches few automated techniques, making it a delayed and error-prone activity. These factors undermine environmental control and decision-making. The use of computer vision is an alternative to automatic recognition, since it allows the development of intelligent systems, in which, from images, are able to detect features and perform a final classification. One of the techniques of Computer Vision is the use of Convolutional Neural Networks, technique that represents the state of the art in this area, it is the construction of models capable of interpreting patterns in images. This research addresses experiments using convolutional neural networks for recognizing forest species from digital images. Two original datasets were used, one including macroscopic images and the other including microscopic images, for which three models were created: scale recognition, species recognition from macroscopic images and species recognition from microscopic. The best models provide 100% recognition rates for the scale dataset, 98.73% for the macroscopic and 99.11% for the microscopic which made possible to develop a software as a final product, using these three models

다중 센싱 플랫폼과 딥러닝을 활용한 도시 규모의 수목 맵핑 및 수종 탐지

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2023. 2. 류영렬.Precise estimation of the number of trees and individual tree location with species information all over the city forms solid foundation for enhancing ecosystem service. However, mapping individual trees at the city scale remains challenging due to heterogeneous patterns of urban tree distribution. Here, we present a novel framework for merging multiple sensing platforms with leveraging various deep neural networks to produce a fine-grained urban tree map. We performed mapping trees and detecting species by relying only on RGB images taken by multiple sensing platforms such as airborne, citizens and vehicles, which fueled six deep learning models. We divided the entire process into three steps, since each platform has its own strengths. First, we produced individual tree location maps by converting the central points of the bounding boxes into actual coordinates from airborne imagery. Since many trees were obscured by the shadows of the buildings, we applied Generative Adversarial Network (GAN) to delineate hidden trees from the airborne images. Second, we selected tree bark photos collected by citizen for species mapping in urban parks and forests. Species information of all tree bark photos were automatically classified after non-tree parts of images were segmented. Third, we classified species of roadside trees by using a camera mounted on a car to augment our species mapping framework with street-level tree data. We estimated the distance from a car to street trees from the number of lanes detected from the images. Finally, we assessed our results by comparing it with Light Detection and Ranging (LiDAR), GPS and field data. We estimated over 1.2 million trees existed in the city of 121.04 km² and generated more accurate individual tree positions, outperforming the conventional field survey methods. Among them, we detected the species of more than 63,000 trees. The most frequently detected species was Prunus yedoensis (21.43 %) followed by Ginkgo biloba (19.44 %), Zelkova serrata (18.68 %), Pinus densiflora (7.55 %) and Metasequoia glyptostroboides (5.97 %). Comprehensive experimental results demonstrate that tree bark photos and street-level imagery taken by citizens and vehicles are conducive to delivering accurate and quantitative information on the distribution of urban tree species.도시 전역에 존재하는 모든 수목의 숫자와 개별 위치, 그리고 수종 분포를 정확하게 파악하는 것은 생태계 서비스를 향상시키기 위한 필수조건이다. 하지만, 도시에서는 수목의 분포가 매우 복잡하기 때문에 개별 수목을 맵핑하는 것은 어려웠다. 본 연구에서는, 여러가지 센싱 플랫폼을 융합함과 동시에 다양한 딥러닝 네트워크들을 활용하여 세밀한 도시 수목 지도를 제작하는 새로운 프레임워크를 제안한다. 우리는 오직 항공사진, 시민, 차량 등의 플랫폼으로부터 수집된 RGB 이미지만을 사용하였으며, 6가지 딥러닝 모델을 활용하여 수목을 맵핑하고 수종을 탐지하였다. 각각의 플랫폼은 저마다의 강점이 있기 때문에 전 과정을 세 가지 스텝으로 구분할 수 있다. 첫째, 우리는 항공사진 상에서 탐지된 수목의 딥러닝 바운딩 박스로부터 중심점을 추출하여 개별 수목의 위치 지도를 제작하였다. 많은 수목이 도시 내 고층 빌딩의 그림자에 의해 가려졌기 때문에, 우리는 생정적 적대적 신경망 (Generative Adversarial Network, GAN)을 통해 항공사진 상에 숨겨진 수목을 그려내고자 하였다. 둘째, 우리는 시민들이 수집한 수목의 수피 사진을 활용하여 도시 공원 및 도시 숲 일대에 수종 정보를 맵핑하였다. 수피 사진으로부터의 수종 정보는 딥러닝 네트워크에 의해 자동으로 분류되었으며, 이 과정에서 이미지 분할 모델 또한 적용되어 딥러닝 분류 모델이 오로지 수피 부분에만 집중할 수 있도록 하였다. 셋째, 우리는 차량에 탑재된 카메라를 활용하여 도로변 가로수의 수종을 탐지하였다. 이 과정에서 차량으로부터 가로수까지의 거리 정보가 필요하였는데, 우리는 이미지 상의 차선 개수로부터 거리를 추정하였다. 마지막으로, 본 연구 결과는 라이다 (Light Detection and Ranging, LiDAR)와 GPS 장비, 그리고 현장 자료에 의해 평가되었다. 우리는 121.04 km² 면적의 대상지 내에 약 130만여 그루의 수목이 존재하는 것을 확인하였으며, 다양한 선행연구보다 높은 정확도의 개별 수목 위치 지도를 제작하였다. 탐지된 모든 수목 중 약 6만 3천여 그루의 수종 정보가 탐지되었으며, 이중 가장 빈번히 탐지된 수목은 왕벚나무 (Prunus yedoensis, 21.43 %)였다. 은행나무 (Ginkgo biloba, 19.44 %), 느티나무 (Zelkova serrata, 18.68 %), 소나무 (Pinus densiflora, 7.55 %), 그리고 메타세쿼이어 (Metasequoia glyptostroboides, 5.97 %) 등이 그 뒤를 이었다. 포괄적인 검증이 수행되었고, 본 연구에서는 시민이 수집한 수피 사진과 차량으로부터 수집된 도로변 이미지는 도시 수종 분포에 대한 정확하고 정량적인 정보를 제공한다는 것을 검증하였다.1. Introduction 6 2. Methodology 9 2.1. Data collection 9 2.2. Deep learning overall 12 2.3. Tree counting and mapping 15 2.4. Tree species detection 16 2.5. Evaluation 21 3. Results 22 3.1. Evaluation of deep learning performance 22 3.2. Tree counting and mapping 23 3.3. Tree species detection 27 4. Discussion 30 4.1. Multiple sensing platforms for urban areas 30 4.2. Potential of citizen and vehicle sensors 34 4.3. Implications 48 5. Conclusion 51 Bibliography 52 Abstract in Korean 61석