Efficient Hybrid Supervision for Instance Segmentation in Aerial Images

Instance segmentation in aerial images is of great significance for remote sensing applications, and it is inherently more challenging because of cluttered background, extremely dense and small objects, and objects with arbitrary orientations. Besides, current mainstream CNN-based methods often suffer from the trade-off between labeling cost and performance. To address these problems, we present a pipeline of hybrid supervision. In the pipeline, we design an ancillary segmentation model with the bounding box attention module and bounding box filter module. It is able to generate accurate pseudo pixel-wise labels from real-world aerial images for training any instance segmentation models. Specifically, bounding box attention module can effectively suppress the noise in cluttered background and improve the capability of segmenting small objects. Bounding box filter module works as a filter which removes the false positives caused by cluttered background and densely distributed objects. Our ancillary segmentation model can locate object pixel-wisely instead of relying on horizontal bounding box prediction, which has better adaptability to arbitrary oriented objects. Furthermore, oriented bounding box labels are utilized for handling arbitrary oriented objects. Experiments on iSAID dataset show that the proposed method can achieve comparable performance (32.1 AP) to fully supervised methods (33.9 AP), which is obviously higher than weakly supervised setting (26.5 AP), when using only 10% pixel-wise labels.

Download Full-text

Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method

Sensors ◽

10.3390/s20216070 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6070

Author(s):

Matheus Gomes ◽

Jonathan Silva ◽

Diogo Gonçalves ◽

Pedro Zamboni ◽

Jader Perez ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Urban Areas ◽

Aerial Images ◽

Detection Methods ◽

Ground Sample ◽

Sensing Applications ◽

Bounding Box ◽

Utility Poles ◽

Remote Sensing Applications

Mapping utility poles using side-view images acquired with car-mounted cameras is a time-consuming task, mainly in larger areas due to the need for street-by-street surveying. Aerial images cover larger areas and can be feasible alternatives although the detection and mapping of the utility poles in urban environments using top-view images is challenging. Thus, we propose the use of Adaptive Training Sample Selection (ATSS) for detecting utility poles in urban areas since it is a novel method and has not yet investigated in remote sensing applications. Here, we compared ATSS with Faster Region-based Convolutional Neural Networks (Faster R-CNN) and Focal Loss for Dense Object Detection (RetinaNet ), currently used in remote sensing applications, to assess the performance of the proposed methodology. We used 99,473 patches of 256 × 256 pixels with ground sample distance (GSD) of 10 cm. The patches were divided into training, validation and test datasets in approximate proportions of 60%, 20% and 20%, respectively. As the utility pole labels are point coordinates and the object detection methods require a bounding box, we assessed the influence of the bounding box size on the ATSS method by varying the dimensions from 30×30 to 70×70 pixels. For the proposal task, our findings show that ATSS is, on average, 5% more accurate than Faster R-CNN and RetinaNet. For a bounding box size of 40×40, we achieved Average Precision with intersection over union of 50% (AP50) of 0.913 for ATSS, 0.875 for Faster R-CNN and 0.874 for RetinaNet. Regarding the influence of the bounding box size on ATSS, our results indicate that the AP50 is about 6.5% higher for 60×60 compared to 30×30. For AP75, this margin reaches 23.1% in favor of the 60×60 bounding box size. In terms of computational costs, all the methods tested remain at the same level, with an average processing time around of 0.048 s per patch. Our findings show that ATSS outperforms other methodologies and is suitable for developing operation tools that can automatically detect and map utility poles.

Download Full-text

Design and performance simulations for an airborne DIAL system for long-range remote sensing applications

10.1117/12.263157 ◽

1997 ◽

Author(s):

James A. Dowling ◽

Brian T. Kelly ◽

John D. Gonglewski ◽

Marsha J. Fox ◽

Michael L. Shilko, Sr. ◽

...

Keyword(s):

Remote Sensing ◽

Long Range ◽

Sensing Applications ◽

Remote Sensing Applications ◽

And Performance

Download Full-text

CPISNet: Delving into Consistent Proposals of Instance Segmentation Network for High-Resolution Aerial Images

Remote Sensing ◽

10.3390/rs13142788 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2788

Author(s):

Xiangfeng Zeng ◽

Shunjun Wei ◽

Jinshan Wei ◽

Zichen Zhou ◽

Jun Shi ◽

...

Keyword(s):

High Resolution ◽

Semantic Segmentation ◽

Aerial Images ◽

False Alarms ◽

Feature Maps ◽

Sensing Applications ◽

Multi Level ◽

Bounding Boxes ◽

Adaptive Feature Extraction ◽

Instance Segmentation

Instance segmentation of high-resolution aerial images is challenging when compared to object detection and semantic segmentation in remote sensing applications. It adopts boundary-aware mask predictions, instead of traditional bounding boxes, to locate the objects-of-interest in pixel-wise. Meanwhile, instance segmentation can distinguish the densely distributed objects within a certain category by a different color, which is unavailable in semantic segmentation. Despite the distinct advantages, there are rare methods which are dedicated to the high-quality instance segmentation for high-resolution aerial images. In this paper, a novel instance segmentation method, termed consistent proposals of instance segmentation network (CPISNet), for high-resolution aerial images is proposed. Following top-down instance segmentation formula, it adopts the adaptive feature extraction network (AFEN) to extract the multi-level bottom-up augmented feature maps in design space level. Then, elaborated RoI extractor (ERoIE) is designed to extract the mask RoIs via the refined bounding boxes from proposal consistent cascaded (PCC) architecture and multi-level features from AFEN. Finally, the convolution block with shortcut connection is responsible for generating the binary mask for instance segmentation. Experimental conclusions can be drawn on the iSAID and NWPU VHR-10 instance segmentation dataset: (1) Each individual module in CPISNet acts on the whole instance segmentation utility; (2) CPISNet* exceeds vanilla Mask R-CNN 3.4%/3.8% AP on iSAID validation/test set and 9.2% AP on NWPU VHR-10 instance segmentation dataset; (3) The aliasing masks, missing segmentations, false alarms, and poorly segmented masks can be avoided to some extent for CPISNet; (4) CPISNet receives high precision of instance segmentation for aerial images and interprets the objects with fitting boundary.

Download Full-text