Dense Dilated Convolutions Merging Network for Land Cover Classification

Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task. The proposed DDCM-Net consists of dense dilated image convolutions merged with varying dilation rates. This effectively utilizes rich combinations of dilated convolutions that enlarge the network's receptive fields with fewer parameters and features compared with the state-of-the-art approaches in the remote sensing domain. Importantly, DDCM-Net obtains fused local- and global-context information, in effect incorporating surrounding discriminative capability for multiscale and complex-shaped objects with similar color and textures in very high-resolution aerial imagery. We demonstrate the effectiveness, robustness, and flexibility of the proposed DDCM-Net on the publicly available ISPRS Potsdam and Vaihingen data sets, as well as the DeepGlobe land cover data set. Our single model, trained on three-band Potsdam and Vaihingen data sets, achieves better accuracy in terms of both mean intersection over union (mIoU) and F1-score compared with other published models trained with more than three-band data. We further validate our model on the DeepGlobe data set, achieving state-of-the-art result 56.2% mIoU with much fewer parameters and at a lower computational cost compared with related recent work. Code available at https://github.com/samleoqh/DDCM-Semantic-Segmentation-PyTorchComment: Semantic Segmentation, 12 pages, TGRS-2020 early access in IEEE Transactions on Geoscience and Remote Sensing. 2020, Code available at https://github.com/samleoqh/DDCM-Semantic-Segmentation-PyTorc

Advancing Land Cover Mapping in Remote Sensing with Deep Learning

Automatic mapping of land cover in remote sensing data plays an increasingly significant role in several earth observation (EO) applications, such as sustainable development, autonomous agriculture, and urban planning. Due to the complexity of the real ground surface and environment, accurate classification of land cover types is facing many challenges. This thesis provides novel deep learning-based solutions to land cover mapping challenges such as how to deal with intricate objects and imbalanced classes in multi-spectral and high-spatial resolution remote sensing data. The first work presents a novel model to learn richer multi-scale and global contextual representations in very high-resolution remote sensing images, namely the dense dilated convolutions' merging (DDCM) network. The proposed method is light-weighted, flexible and extendable, so that it can be used as a simple yet effective encoder and decoder module to address different classification and semantic mapping challenges. Intensive experiments on different benchmark remote sensing datasets demonstrate that the proposed method can achieve better performance but consume much fewer computation resources compared with other published methods. Next, a novel graph model is developed for capturing long-range pixel dependencies in remote sensing images to improve land cover mapping. One key component in the method is the self-constructing graph (SCG) module that can effectively construct global context relations (latent graph structure) without requiring prior knowledge graphs. The proposed SCG-based models achieved competitive performance on different representative remote sensing datasets with faster training and lower computational cost compared to strong baseline models. The third work introduces a new framework, namely the multi-view self-constructing graph (MSCG) network, to extend the vanilla SCG model to be able to capture multi-view context representations with rotation invariance to achieve improved segmentation performance. Meanwhile, a novel adaptive class weighting loss function is developed to alleviate the issue of class imbalance commonly found in EO datasets for semantic segmentation. Experiments on benchmark data demonstrate the proposed framework is computationally efficient and robust to produce improved segmentation results for imbalanced classes. To address the key challenges in multi-modal land cover mapping of remote sensing data, namely, 'what', 'how' and 'where' to effectively fuse multi-source features and to efficiently learn optimal joint representations of different modalities, the last work presents a compact and scalable multi-modal deep learning framework (MultiModNet) based on two novel modules: the pyramid attention fusion module and the gated fusion unit. The proposed MultiModNet outperforms the strong baselines on two representative remote sensing datasets with fewer parameters and at a lower computational cost. Extensive ablation studies also validate the effectiveness and flexibility of the framework

Weakly Supervised Semantic Segmentation of Satellite Images

International audienceWhen one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works with aerial or satellite images, which are usually very large, it is even worse. With that in mind, we investigate how to use image-level annotations in order to perform semantic segmentation. Image-level annotations are much less expensive to acquire than pixel-level annotations, but we lose a lot of information for the training of the model. From the annotations of the images, the model must find by itself how to classify the different regions of the image. In this work, we use the method proposed by Anh and Kwak [1] to produce pixel-level annotation from image level annotation. We compare the overall quality of our generated dataset with the original dataset. In addition, we propose an adaptation of the AffinityNet that allows us to directly perform a semantic segmentation. Our results show that the generated labels lead to the same performances for the training of several segmentation networks. Also, the quality of semantic segmentation performed directly by the AffinityNet and the Random Walk is close to the one of the best fully-supervised approaches

RSVQA: Visual Question Answering for Remote Sensing Data

This paper introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method introduced in this article, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task. We report the results obtained by applying a model based on Convolutional Neural Networks (CNNs) for the visual part and on a Recurrent Neural Network (RNN) for the natural language part to this task. The model is trained on the two datasets, yielding promising results in both cases.Comment: 12 pages, Published in IEEE Transactions on Geoscience and Remote Sensing. Added one experiment and authors' biographie