Satellite Imagery Multiscale Rapid Detection with Windowed Networks

Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels and geographical extent per image: a single DigitalGlobe satellite image encompasses over 64 km2 and over 250 million pixels. Another challenge is that objects of interest are often minuscule (~pixels in extent even for the highest resolution imagery), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (SIMRDWN) that evaluates satellite images of arbitrarily large size at native resolution at a rate of > 0.2 km2/s. Building upon the tensorflow object detection API paper, this pipeline offers a unified approach to multiple object detection frameworks that can run inference on images of arbitrary size. The SIMRDWN pipeline includes a modified version of YOLO (known as YOLT), along with the models of the tensorflow object detection API: SSD, Faster R-CNN, and R-FCN. The proposed approach allows comparison of the performance of these four frameworks, and can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. For objects of very different scales (e.g. airplanes versus airports) we find that using two different detectors at different scales is very effective with negligible runtime cost.We evaluate large test images at native resolution and find mAP scores of 0.2 to 0.8 for vehicle localization, with the YOLT architecture achieving both the highest mAP and fastest inference speed.Comment: 8 pages, 7 figures, 2 tables, 1 appendix. arXiv admin note: substantial text overlap with arXiv:1805.0951

A Chandra Proper Motion for PSR J1809-2332

We report on a new Chandra exposure of PSR J1809-2332, the recently discovered pulsar powering the bright EGRET source 3EG J1809-2328. By registration of field X-ray sources in an archival exposure, we measure a significant proper motion for the pulsar point source over an ~11 year baseline. The shift of 0.30+/-0.06" (at PA= 153.3+/-18.4) supports an association with proposed SNR parent G7.5-1.7. Spectral analysis of diffuse emission in the region also supports the interpretation as a hard wind nebula trail pointing back toward the SNR.Comment: To Appear in the Astrophysical Journal, Sept 1 (v. 756

Rings and Jets around PSR J2021+3651: the `Dragonfly Nebula'

We describe recent Chandra ACIS observations of the Vela-like pulsar PSR J2021+3651 and its pulsar wind nebula (PWN). This `Dragonfly Nebula' displays an axisymmetric morphology, with bright inner jets, a double-ridged inner nebula, and a ~30" polar jet. The PWN is embedded in faint diffuse emission: a bow shock-like structure with standoff ~1' brackets the pulsar to the east and emission trails off westward for 3-4'. Thermal (kT=0.16 +/-0.02 keV) and power law emission are detected from the pulsar. The nebular X-rays show spectral steepening from Gamma=1.5 in the equatorial torus to Gamma=1.9 in the outer nebula, suggesting synchrotron burn-off. A fit to the `Dragonfly' structure suggests a large (86 +/-1 degree) inclination with a double equatorial torus. Vela is currently the only other PWN showing such double structure. The >12 kpc distance implied by the pulsar dispersion measure is not supported by the X-ray data; spectral, scale and efficiency arguments suggest a more modest 3-4 kpc.Comment: 22 pages, 5 figures, 3 tables, Accepted to Ap

SpaceNet MVOI: a Multi-View Overhead Imagery Dataset

Detection and segmentation of objects in overheard imagery is a challenging task. The variable density, random orientation, small size, and instance-to-instance heterogeneity of objects in overhead imagery calls for approaches distinct from existing models designed for natural scene datasets. Though new overhead imagery datasets are being developed, they almost universally comprise a single view taken from directly overhead ("at nadir"), failing to address a critical variable: look angle. By contrast, views vary in real-world overhead imagery, particularly in dynamic scenarios such as natural disasters where first looks are often over 40 degrees off-nadir. This represents an important challenge to computer vision methods, as changing view angle adds distortions, alters resolution, and changes lighting. At present, the impact of these perturbations for algorithmic detection and segmentation of objects is untested. To address this problem, we present an open source Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of these images cover the same 665 square km geographic extent and are annotated with 126,747 building footprint labels, enabling direct assessment of the impact of viewpoint perturbation on model performance. We benchmark multiple leading segmentation and object detection models on: (1) building detection, (2) generalization to unseen viewing angles and resolutions, and (3) sensitivity of building footprint extraction to changes in resolution. We find that state of the art segmentation and object detection models struggle to identify buildings in off-nadir imagery and generalize poorly to unseen views, presenting an important benchmark to explore the broadly relevant challenge of detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV) 201