Information on cultivated crops is relevant for a large number of food security studies. Different scientific efforts are dedicated to generating this information from remote sensing images by means of machine learning methods. Unfortunately, these methods do not take account of the spatial-temporal relationships inherent in remote sensing images. In our paper, we explore the capability of a 3D Fully Convolutional Neural Network (FCN) to map crop types from multi-temporal images. In addition, we propose the Intersection Over Union (IOU) loss function for increasing the overlap between the predicted classes and ground reference data. The proposed method was applied to identify soybean and corn from a study area situated in the US corn belt using multi-temporal Landsat images. The study shows that our method outperforms related methods, obtaining a Kappa coefficient of 91.8%. We conclude that using the IOU loss function provides a superior choice to learn individual crop types.
The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses. We present a method for direct optimization of the mean intersection-over-union loss in neural networks, in the context of semantic image segmentation, based on the convex Lovász extension of submodular losses. The loss is shown to perform better with respect to the Jaccard…
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）
Bounding box (bbox) regression is a fundamental task in computer vision. So far, the most commonly used loss functions for bbox regression are the Intersection over Union (IoU) loss and its variants. In this paper, we generalize existing IoU-based losses to a new family of power IoU losses that have a power IoU term and an additional power regularization term with a single power parameter $α$. We call this new family of losses the $α$-IoU losses and analyze properties such as order preservingness and loss/gradient reweighting. Experiments on multiple object detection benchmarks and models demo…
The Hong Kong Polytechnic University（香港理工大学）；DJI Co.,Ltd（大疆创新科技有限公司）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruth, which cannot reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of cropping models, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the special properties an…
University of Ostrava（俄斯特拉发大学）；Centre of Excellence IT4Innovations（IT4创新卓越中心）；Institute for Research and Applications of Fuzzy Modeling（模糊建模研究与应用研究所）
We focus on the construction of a loss function for the bounding box regression. The Intersection over Union (IoU) metric is improved to converge faster, to make the surface of the loss function smooth and continuous over the whole searched space, and to reach a more precise approximation of the labels. The main principle is adding a smoothing part to the original IoU, where the smoothing part is given by a linear space with values that increases from the ground truth bounding box to the border of the input image, and thus covers the whole spatial search space. We show the motivation and forma…
The Hong Kong Polytechnic University（香港理工大学）；DJI Innovations Co., Ltd（大疆创新科技有限公司）
Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Most of the existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruths, which can hardly reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the spe…
Tree crop mapping of South America reveals links to deforestation and conservation
Google DeepMind（谷歌DeepMind）；EcoVision Lab, DM3L, University of Zurich（EcoVision实验室、DM3L、苏黎世大学）；University of Zurich（苏黎世大学）；University of Copenhagen（哥本哈根大学）；Google Research（谷歌研究）；ETH Zürich（苏黎世联邦理工学院）
SUIT: Spatial-Spectral Union-Intersection Interaction Network for Hyperspectral Object Tracking
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；College of Computer Science, Zhejiang University（浙江大学计算机科学学院）
Stanford University（斯坦福大学）；The University of Adelaide（阿德莱德大学）；Aibee Inc（Aibee公司）
Intersection over Union (IoU) is the most popular evaluation metric used in the object detection benchmarks. However, there is a gap between optimizing the commonly used distance losses for regressing the parameters of a bounding box and maximizing this metric value. The optimal objective for a metric is the metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown that $IoU$ can be directly used as a regression loss. However, $IoU$ has a plateau making it infeasible to optimize in the case of non-overlapping bounding boxes. In this paper, we address the weaknesses of $IoU$…
EC-IoU: Orienting Safety for Object Detectors via Ego-Centric Intersection-over-Union
DENSO AUTOMOTIVE Deutschland GmbH（电装德国汽车有限公司）；Technical University of Munich（慕尼黑工业大学）；Chalmers University of Technology（查尔姆斯理工大学）；University of Gothenburg（哥德堡大学）
GAF AG（GAF股份公司）；Technical University of Munich (TUM)（慕尼黑工业大学）
We present EuroCrops, a dataset based on self-declared field annotations for training and evaluating methods for crop type classification and mapping, together with its process of acquisition and harmonisation. By this, we aim to enrich the research efforts and discussion for data-driven land cover classification via Earth observation and remote sensing. Additionally, through inclusion of self-declarations gathered in the scope of subsidy control from all countries of the European Union (EU), this dataset highlights the difficulties and pitfalls one comes across when operating on a transnation…
University of Lincoln（林肯大学）；Flanders Make（法兰德斯制造研究院）；Ghent University（根特大学）；Flanders Research Institute for Agriculture, Fisheries and Food (ILVO)（法兰德斯农业、渔业与食品研究所）；Swedish University of Agricultural Sciences（瑞典农业科学大学）
Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same sem…
Chalmers University of Technology（查尔姆斯理工大学）；Zenuity AB（Zenuity公司）
Three-dimensional object detection from a single view is a challenging task which, if performed with good accuracy, is an important enabler of low-cost mobile robot perception. Previous approaches to this problem suffer either from an overly complex inference engine or from an insufficient detection accuracy. To deal with these issues, we present SS3D, a single-stage monocular 3D object detector. The framework consists of (i) a CNN, which outputs a redundant representation of each relevant object in the image with corresponding uncertainty estimates, and (ii) a 3D bounding box optimizer. We sh…
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union
KU Leuven（鲁汶大学）；Google（谷歌公司）；Google DeepMind（谷歌DeepMind）；University of Oxford（牛津大学）；EPFL（洛桑联邦理工学院）；ETH Zürich（苏黎世联邦理工学院）；INSAIT（INSAIT研究院）；Samsung Research（三星研究院）
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery
Coordinación General de Innovación Gubernamental, del Gobierno de Jalisco（哈利斯科州政府政府创新总协调局）；ITESO—The Jesuit University of Guadalajara（瓜达拉哈拉伊泰索耶稣会大学）；Universidad Autónoma de Guadalajara（瓜达拉哈拉自治大学）
Tel Aviv University（特拉维夫大学）；Facebook AI Research（脸书人工智能研究院）
We present a method for recovering the shared content between two visual domains as well as the content that is unique to each domain. This allows us to map from one domain to the other, in a way in which the content that is specific for the first domain is removed and the content that is specific for the second is imported from any image in the second domain. In addition, our method enables generation of images from the intersection of the two domains as well as their union, despite having no such samples during training. The method is shown analytically to contain all the sufficient and nece…
