In he past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird’s-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.

This letter presents a method for satellite image classification aiming at the following two objectives: 1) involving visual attention into the satellite image classification; biologically inspired saliency information is exploited in the phase of the image representation, making our method more concentrated on the interesting objects and structures, and 2) handling the satellite image classification without the learning phase. A two-layer sparse coding (TSC) model is designed to discover the “true” neighbors of the images and bypass the intensive learning phase of the satellite image classification. The underlying philosophy of the TSC is that an image can be more sparsely reconstructed via the images (sparse I) belonging to the same category (sparse II). The images are classified according to a newly defined “image-to-category” similarity based on the coding coefficients. Requiring no training phase, our method achieves very promising results. The experimental comparisons are shown on a real satellite image database.

The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as an essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million Aerial Image Dataset (Online. Available: https://captain-whu.github.io/DiRS/0), a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this article will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.

This article presents a new method for high-resolution satellite scene classification. Specifically, we make three main contributions: (1) we introduce the sparse coding method for satellite scene classification; (2) we present local ternary pattern histogram Fourier (LTP-HF) features, an improved rotation invariant texture descriptor based on LTPs; (3) we effectively combine a set of diverse and complementary features to further improve the performance. A two-stage linear support vector machine (SVM) classifier is designed for this purpose. In the first stage, the SVM is used to generate probability images with a scale invariant feature transform (SIFT), LTP-HF and colour histogram features, respectively. The generated probability images with different features are fused in the second stage in order to obtain the final classification results. Experimental results show that the suggested classification method achieves very promising performance.

One fundamental problem in Earth Vision is to accurately find the locations and identify the categories of the interesting objects in the aerial images, for which oriented bounding boxes (OBBs) are usually employed to depict better the objects emerging with arbitrary orientations. However, the regression of the OBBs always suffers from the ambiguous problem in the definition of the regression targets, which often reduces the convergency efficiency and decreases the detection accuracy. Although there are some methods like the binary segmentation map that can handle this problem, it brings a new problem of ambiguous background pixels in the OBBs. In this article, we propose to cast the OBB regression as a center-probability-map (CenterMap)-prediction problem, thus largely eliminating the ambiguities on the target definitions and the background pixels. The predicted CenterMaps are then used to generate the OBBs. The CenterMap OBB representation is simple, yet effective. Furthermore, to distinguish better the interesting objects from the cluttered background, a weighted pseudosegmentation-guided attention network is adopted to provide the object-level features for predicting the horizontal bounding boxes and the OBBs. The experimental results on three widely used data sets, i.e., DOTA, HRSC2016, and UCAS-AOD, demonstrate the effectiveness of our proposed method.