Remote Sensing Image Scene Classification: Benchmark and State of the Art

Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention. During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a systematic review of the literature concerning datasets and methods for scene classification is still lacking. In addition, almost all existing datasets have a number of limitations, including the small scale of scene classes and the image numbers, the lack of image variations and diversity, and the saturation of accuracy. These limitations severely limit the development of new approaches especially deep learning-based methods. This paper first provides a comprehensive review of the recent progress. Then, we propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class. The proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total image number, (ii) holds big variations in translation, spatial resolution, viewpoint, object pose, illumination, background, and occlusion, and (iii) has high within-class diversity and between-class similarity. The creation of this dataset will enable the community to develop and evaluate various data-driven algorithms. Finally, several representative methods are evaluated using the proposed dataset and the results are reported as a useful baseline for future research.Comment: This manuscript is the accepted version for Proceedings of the IEE

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network (CNN) and a recurrent neural network (RNN) into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependency in bi-temporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) It is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; 3) it is capable of adaptively learning the temporal dependency between multitemporal images, unlike most of algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analysis of experimental results demonstrates competitive performance in the proposed mode

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Large Area Land Cover Mapping Using Deep Neural Networks and Landsat Time-Series Observations

This dissertation focuses on analysis and implementation of deep learning methodologies in the field of remote sensing to enhance land cover classification accuracy, which has important applications in many areas of environmental planning and natural resources management. The first manuscript conducted a land cover analysis on 26 Landsat scenes in the United States by considering six classifier variants. An extensive grid search was conducted to optimize classifier parameters using only the spectral components of each pixel. Results showed no gain in using deep networks by using only spectral components over conventional classifiers, possibly due to the small reference sample size and richness of features. The effect of changing training data size, class distribution, or scene heterogeneity were also studied and we found all of them having significant effect on classifier accuracy. The second manuscript reviewed 103 research papers on the application of deep learning methodologies in remote sensing, with emphasis on per-pixel classification of mono-temporal data and utilizing spectral and spatial data dimensions. A meta-analysis quantified deep network architecture improvement over selected convolutional classifiers. The effect of network size, learning methodology, input data dimensionality and training data size were also studied, with deep models providing enhanced performance over conventional one using spectral and spatial data. The analysis found that input dataset was a major limitation and available datasets have already been utilized to their maximum capacity. The third manuscript described the steps to build the full environment for dataset generation based on Landsat time-series data using spectral, spatial, and temporal information available for each pixel. A large dataset containing one sample block from each of 84 ecoregions in the conterminous United States (CONUS) was created and then processed by a hybrid convolutional+recurrent deep network, and the network structure was optimized with thousands of simulations. The developed model achieved an overall accuracy of 98% on the test dataset. Also, the model was evaluated for its overall and per-class performance under different conditions, including individual blocks, individual or combined Landsat sensors, and different sequence lengths. The analysis found that although the deep model performance per each block is superior to other candidates, the per block performance still varies considerably from block to block. This suggests extending the work by model fine-tuning for local areas. The analysis also found that including more time stamps or combining different Landsat sensor observations in the model input significantly enhances the model performance