A Comparison of Super-Resolution and NearestNeighbors Interpolation Applied to Object

Detection on Satellite DataEvan Koester

Systems and Technology ResearchWoburn, Massachusetts, USAevan.koester@stresearch.com

Cem Safak SahinSystems and Technology Research

Woburn, Massachusetts, USAcem.sahin@stresearch.com

Abstract—As Super-Resolution (SR) has matured as a researchtopic, it has been applied to additional topics beyond imagereconstruction. In particular, combining classification or objectdetection tasks with a super-resolution preprocessing stage hasyielded improvements in accuracy especially with objects thatare small relative to the scene. While SR has shown promise, astudy comparing SR and naive upscaling methods such as NearestNeighbors (NN) interpolation when applied as a preprocessingstep for object detection has not been performed. We apply thetopic to satellite data and compare the Multi-scale Deep Super-Resolution (MDSR) system to NN on the xView challenge dataset.To do so, we propose a pipeline for processing satellite data thatcombines multi-stage image tiling and upscaling, the YOLOv2object detection architecture, and label stitching. We comparethe effects of training models using an upscaling factor of 4,upscaling images from 30cm Ground Sample Distance (GSD) toan effective GSD of 7.5cm. Upscaling by this factor significantlyimproves detection results, increasing Average Precision (AP)of a generalized vehicle class by 23 percent. We demonstratethat while SR produces upscaled images that are more visuallypleasing than their NN counterparts, object detection networkssee little difference in accuracy with images upsampled usingNN obtaining nearly identical results to the MDSRx4 enhancedimages with a difference of 0.0002 AP between the two methods.

Index Terms—upscaling, super resolution, object detection,aerial, satellite

I. INTRODUCTION

With machine vision and deep Convolutional Neural Net-works (CNNs) being applied to novel problems and data, therehas been rapid growth in both network architecture advance-ment and the applications with which these networks are beingapplied. However, in most classification and object detectionapplications the image data is such that objects of interestare large with respect to the scene. This can be observed inthe most popular public benchmark datasets ImageNet, VOC,COCO, and CIFAR [1][2][3][4]. While these datasets and theirrespective challenges have continued to produce incremen-tally advancing network architectures including SqueezeNets,Squeeze-and-Excitation Networks, and Faster R-CNN, an openchallenge remains in data where objects are small relative to

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a larger scene [5][6][7]. This holds true for satellite data withDigitalGlobe’s WorldView-3 satellite representing each pixelas a 30cm2 area.

In these scenes, objects such as cars are often 13x7 pixelsor less and are in scenes larger than 3000x3000 in size.These large scenes require preprocessing to be used in modernobject detection networks that includes tiling the originalscenes into smaller components for training and validation.To add to this, objects such as vehicles are often located closetogether in areas such as parking lots and busy roadways,making the boundaries between vehicles difficult to perceivein satellite scenes. A lack of publicly available labeled datahas also hindered exploration into this application space, withonly the xView Challenge Dataset having satellite capturedimagery with labeled objects such as vehicles [8]. Other aerialdatasets such as Classification of Fine-Grained features inAerial images (COFGA), A large-scale Dataset for ObjectDetection in Aerial images (DOTA), and Cars Overhead WithContext (COWC) have similar object classes, but exist at alower Ground Sample Distance (GSD) making them easierto obtain good object detection results but limiting real-worldapplications [9][10][11].

Given the challenges of applying CNNs to satellite data,performing upscaling as a preprocessing step is essential toachieving good performance in accurately detecting objects.Modern advances in deep learning have resulted in a numberof advanced architectures to perform upscaling that train anetwork on a low-resolution image and validate it versus ahigh-resolution copy. Despite a growing body of literatureon the subject, the application of Super-Resolution (SR) toobject detection and classification problems has been largelyunexplored and essential comparisons between SR and naiveupscaling such as Nearest Neighbors (NN) interpolation havenot been documented in the literature. While SR networksshow promise as a preprocessing step for object detection insatellite imagery, they also add a significant computational costdue to their deep networks containing millions of parametersthat must be correctly trained. Unlike SR, NN remains oneof the most basic methods of upscaling and performs interpo-lation by taking a neighboring pixel and assuming its value,

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creating a piece-wise step function approximation with littlecomputational cost.

In this paper, we present novel contributions to the growingliterature in object detection and super-resolution. Througha study of SR and NN upscaling in training and validatingan object detection network applied to satellite imagery, weaim to determine the benefits of application-driven SR tooffer a quantitative comparison of SR to basic interpolationmethods as applied to object detection tasks. To achieve this,we propose a novel multi-stage pipeline to tile, upscale, andfurther tile pan-sharpened WorldView-3 satellite images intoresolution-enhanced components. In doing so, we solve adocumented issue with the You Only Look Once (YOLO)architecture in overcoming its challenge in accurately iden-tifying large clusters of small objects [12]. We compare amodel trained on super-resolved data to models trained onimages of native resolution, NN upscaled by a factor of two,and NN upscaled by a factor of four. In doing so, we areable to evaluate the value of SR in real-world machine visionapplications and whether it shows enough benefit to outweighthe large computational costs associated with a platform inwhich multiple deep learning architectures coexist.

II. RELATED WORK

A. Object Detection Architectures

The application of object detection networks to satelliteimages presents a unique challenge in that satellite imagesare extremely large, making single objects in a scene difficultto localize and detect. While images of this kind require pre-processing to reduce the image input size to the object detec-tion network, the detection framework chosen plays a largerole in the accuracy and computational cost of performingobject detection in this domain.

Modern object detection frameworks are typically split intosingle-stage and two-stage architectures, with the most popularof the first category including SSD, ResNet, and You OnlyLook Once (YOLO) [13][14][12]. These networks rely ona combination of anchor box and sliding window methodsto perform region proposal and feature extraction from ascene simultaneously. A significant advantage of single-stagemodels is that they are designed to perform object detectionquickly, with YOLO obtaining near-real time detection speeds.In the satellite domain, this translates to significant savings indetection time and computational cost.

Faster R-CNN and Mask R-CNN are common two-stagearchitectures that separate the region proposal and featureextraction components into separate stages and share resultsbetween stages to reduce computational load [7][15]. Whilethese networks are more computationally expensive, they hadthe advantage of performance when compared to their single-stage counterparts. However advances in single-stage networkshave been shown to outperform two-stage networks whileperforming detection significantly faster [16].

The most recent advances in object detection architec-tures have centered around improving the loss function thatthese networks use to optimize their weights. Of note are

advances such as ADAM, Focal Loss, Predefined Evenly-Distributed Class Centroids Loss, and Reduced Focal Loss[17][18][19][20]. Reduced Focal Loss was used by the winnersof the xView object detection in satellite imagery competition.

B. Super Resolution and Overhead Image Applications

SR networks have demonstrated the remarkable capabilityto upscale images and provide greatly improved visual per-ceptibility to humans compared to traditional interpolationmethods such as NN or bicubic interpolation [21]. Thesenetworks train on low-resolution images to learn a parameterspace to be able to approximate an upscaled version of theimage that closely matches a held-out high-resolution copy.A variety of SR networks exist, including Super ResolutionGenerative Adversarial Network (SRGAN), Enhanced DeepSuper Resolution (EDSR), Deep Back Projection Networks(DBPN), Super Resolution DenseNets, and Deep LaplacianPyramid Networks (DLPN) [21][22][23][24][25]. While eachof these networks has a different architecture, all are attempt-ing the problem of single-image super-resolution on commonSR datasets such as Set14, B100, and DIV2K[26][27][28].

Much of the research in SR has focused on comparingdifferent SR architectures, usually using a Picture Signal-To-Noise Ratio (PSNR) metric to evaluate how closely anarchitecture can reconstruct the true high-resolution imagefrom a low-resolution image. For this reason, these networksare rarely evaluated on the basis of computational cost, thoughwork by Shi et. al. attempts to perform SR upscaling in nearreal-time using a sub-pixel CNN architecture [29]. Researchinto the applications of SR as a preprocessing stage for objectdetection and classification is much less studied, though initialstudies have shown that the use of SR as a preprocessingstep can yield significant improvements to the detection andclassification of small objects within larger images versusdetecting the objects at their native resolution [30].

For overhead images, super-resolution has been shown toboth improve images visually and enhance object detectorperformance. Bosch investigated SR for overhead imagerythat demonstrated visibly improved results on low-resolutionimages of airplanes [31]. This paper compared the PSNR ofmultiple SR networks to offer a comparison in the overheadimagery domain. While the SR enhanced images are visuallycompelling, this paper fails to apply the output super-resolvedimages to any application.

However, PSNR does not necessarily reflect improvementsin applications to machine vision tasks such as image classifi-cation or object detection. Haris demonstrated this by attachingan SR network to an object detector’s loss function andoptimizing the SR network to minimize object detection loss[32]. Using early stopping, this would end training when theobject detector reached peak performance. In doing so, Harisdemonstrated that the best object detection performance occursa lower PSNR than the maximum achievable PSNR.

In determining the utility of SR in object detection, Sher-meyer and Etten simulated various GSDs on the xView satel-lite image dataset and applied SR architectures to compare

object detection performance across a variety of resolutionsand models [30]. This study was the first of its kind to linkobject detection and SR on satellite imagery and demonstratedthat SR provides significant enhancement in object detectionon small objects such as vehicles and boats. This is echoedby Ferdous who applied SR and an SSD object detectionmodel to the task of vehicle detection on the VEDAI aerialdataset [33]. While both of these studies showed that SRoffers improvements to object detection, they also neglectedto compare the effects of naive upsampling methods such asNN or bicubic interpolation on object detection performance.

III. EXPERIMENTAL SETUP

A. The xView Dataset

To train and validate our models using super-resolution andnearest neighbors interpolation, the xView challenge datasetwas used. This dataset is composed of 1127 unique imagescaptured using the DigitalGlobe World-View 3 satellite. Eachimage is captured at a GSD of 30 centimeters, causingsmall objects such as cars to be represented by a grid ofapproximately 13x7 pixels with few meaningful features forfeature extraction. Because of this, the xView dataset providesgreat opportunity for testing the ability of object detectorsto detect small objects such as vehicles. Approximately onemillion annotated objects are present in the dataset. While1127 images exist in this dataset, labels only exist for 846images. This is because the xView dataset is a competitiondataset, with the organizers withholding the labels of 281images to evaluate competition entries.

(a) (b) (c)

Fig. 1: Example xView images including scenes such asairports (a), cities (b), and industrial areas (c)

One highlight of the xView dataset is its range of uniquelabeled classes with objects such as Fixed-Wing Aircraft,Tugboat, Locomotive and Passenger Vehicle. In total, 60classes are present with many being fine-grained classes ofgeneral object types. An example of this can be evidenced inlabeled objects for Truck, Cargo Truck, Haul Truck and DumpTruck, all representing a general class for Truck. As such,we merge a total of 22 classes together to form a generalvehicle class. To encourage class discrimination in training,we also train the model to recognize an airplane class and ahelicopter class, though our class of interest is solely vehicles.The airplane class is represented by merging the Fixed-wingAircraft, Small Aircraft, and Cargo Plane classes.

We split the 846 labeled images into a training set of 676images and a validation set of 170 images for an 80/20 split.We evaluate our models using an Average Precision (AP)metric across the (vehicle) class and present Precision-Recall(PR) curves and quantitative images for each model.

B. Object Detection and Super Resolution Models

While many methods exist for SR, we leverage the ensembleextension of the state-of-the-art architecture of EDSR, Multi-Scale Deep Super-Resolution (MDSR). The MDSR networkhas the added advantage of performing 2x, 3x, and 4x up-scaling in one model, allowing for us to expand upon theexperiments detailed in this paper. This method combinesresidual learning techniques with an approach that increasesthe width of the network and reducing the depth. We im-plement the PyTorch version of EDSR/MDSR and use theMDSRx4 upscaling to compare against a NN upscaling ap-proach. We can see in Fig. 2 that MDSR produces a morevisually pleasing upscale than the NN interpolation approach.This suggests that the MDSR network succeeds in producingan upscale that has a better PSNR than NN. We leveragethe pre-trained MDSR weights, as we have found the PSNRof these weights are comparable to those of a fully trainedMDSR network using low-resolution images decimated fromthe original xView dataset.

(a) (b) (c)

Fig. 2: Upscaling a Single xView Object. We extracted a 48x48cut from the original scene (a) to perform upscaling by a factorof 4 with MDSR (b) and Nearest Neighbors (c)

To perform our object detection experiments, we leveragethe PyTorch implementation of the You Only Look Once(YOLO) v2 architecture. Satellite imagery covers extremelylarge areas, making fast computation of objects a requirement.This single-stage architecture performs scene detection signif-icantly faster than more complex two-stage networks such asMask R-CNN while achieving similar performance.

We train our YOLOv2 model on two Nvidia Titan Xpgraphics cards with a batch size of 32 and subdivision sizeof 8. We use a learn rate of 0.00025 and momentum of 0.9.The official Darknet-19 pre-trained convolutional weights pre-trained on the ImageNet dataset are used to initialize ourmodel. During training, we include data augmentation to addrandom adjustments to hue, saturation, and exposure to makeour models robust to variation in color and lighting. Randomtranslation, scale, and jitter are also used in training to increasethe robustness of the model. We use a single Nvidia Titan Xp

for performing object detection and evaluating performanceof the trained model. To obtain our Average Precision metricwe use an IOU threshold of 0.5 and sweep across boundingbox confidence thresholds to obtain precision and recall valuesacross all confidence scores from 0.01 to 0.9.

C. A Custom Pipeline for Object Detection on Small Objectswithin Satellite Scenes

To obtain strong performance on satellite imagery, wepropose a multi-stage preprocessing pipeline that combinesimage tiling and upscaling. Images in the xView dataset areextremely large, with each image approximately 4000x3000in size. Given that the YOLOv2 network resizes all inputimages to 416x416 to ensure a 13x13 output feature map,the original xView images must be tiled into smaller imagesto retain objects. Without this step, extremely small objectssuch as vehicles would be resized to a size of less than 4x4.

Our object detection pipeline is tuned for satellite imagery inthat it performs two tiling stages combined with an upscalingstep. For each image in the xView dataset, successive 208x208tiles are cut from the image with an overlap between tiles of 50pixels. These are fed into an upscaling stage, which enlargeseach tile by a factor of 4 to produce an 832x832 cut. Theseupscaled images are then applied to a second stage tiler whichtakes 416x416 cuts with an overlap of 50 pixels. The tilingscheme is such that successive tiles are taken top to bottomuntil the edge of the image is reached, at which point the tilerreturns to the top of the image and shifted to the right. Thiscauses significant overlap at the edges of an image, thoughit is not typically noticeable for large scenes. In the secondtiling stage, this significant overlap becomes an added benefitto performance by allowing YOLOv2 to have a second chanceat detecting objects within a scene, often correctly detectingobjects in one overlapping tile that had been missed in another.The resulting detections are then stitched back together toreturn bounding box locations with respect to the originalscene.

Fig. 3: Object Detection Pipeline using multi-stage tiling,upscaling, YOLO9000 and stitching

To eliminate overlapping bounding boxes that occur inoverlapping tile scenes, we use an Intersection over Area(IOA) metric.

IOA =Intersectionx + Intersectiony

min(Box1area, Box2area)(1)

where

Intersectionx = min(Box1xmax , Box2xmax)

−max(Box1xmin , Box2xmin)(2)

Intersectiony = min(Box1ymax, Box2ymax

)

−max(Box1ymin, Box2ymin

)(3)

and box area is

BoxArea = (xmax − xmin) ∗ (ymax − ymin) (4)

Bounding boxes are sorted by YOLOv2’s confidence score,and then compared to others that are in its vicinity. Weset the threshold so that if a box overlaps another with anintersectional area greater than 75 percent of the area of thebounding box, the box with the lower confidence score isremoved. Our use of an IOA metric differs from many standardapproaches that call for an Intersection-Over-Union (IOU), aswe have found that IOU with an appropriate threshold valuedoes not perform as well as IOA in merging repeated boundingboxes while retaining the bounding boxes of objects locatedclose together, such as trucks, especially when these objectsare positioned at an angle.

IV. RESULTS

In comparing object detection results on vehicles in thexView dataset, it is clear that tiling and upscaling plays asignificant role in improving model performance on satelliteimagery. As demonstrated in Table 1 and Fig. 4, NN interpo-lation by a factor of 2 demonstrates an improvement of 3.4%in AP compared to the tiled images without any upscaling.Using our proposed pipeline and upscaling by a factor of 4 incombination with multi-stage tiling increases performance by22.8% compared to the native image GSD to reach 0.683 AP.

TABLE I: xView Vehicle Detection AP Scores by Method

Method AP Score

1-Stage Tiling 0.4548

1-Stage Tiling, NNx2 0.4889

2-Stage Tiling, MDSRx4 0.6833

2-Stage Tiling, NNx4 0.6835

There were hardly any differences between MDSR and NNupscaling methods, with NNx4 outperforming MDSRx4 by0.02% in AP. Indeed when viewing the Precition-Recall curvein Fig. 4, the MDSRx4 and NNx4 curves are overlapping,suggesting that the object detector is not extracting uniquefeatures from the SR processed images that were not presentin the NNx4 images.

Fig. 4: PR Curve demonstrating the effects of 1-Stage tilingand 2-Stage tiling with different upscaling methods

Given that the YOLOv2 model uses weights pretrained onImageNet which are then refined on objects in the xViewdataset, we suspect that the benefit we see from upscalingobjects with MDSR and NN is largely due to the objects bettermatching ImageNet objects with regards to size in the scene.As a result, upscaling is used to aid the feature extractionprocess that is learned from the pretraining. Because thefeatures are limited in satellite scenes, it is likely the networkis extrapolating features from the limited geometries present inthese small objects as opposed to any unique features broughtabout by advanced upscaling methods such as MDSR.

We note that our proposed object detection pipeline per-forms extremely well in scenes of low object density, asdemonstrated in Fig. 4, and that this holds true for bothMDSRx4 and NNx4 upscaled scenes. In crowded scenes suchas roads, highways, and parking lots, our pipeline performswell though a dip in performance is noted as the boundariesbetween clusters of cars becomes difficult to perceive. Giventhat dense clusters of small objects are a documented challengefor the YOLO object detection architecture, our pipeline’sability to overcome this network shortcoming and maintainhigh accuracy and fast inference speeds is a novel improve-ment towards applying the YOLO architecture to real-worldapplications.

V. CONCLUSION

In this paper we performed a novel comparison betweena modern state-of-the-art SR network to a common naiveinterpolation method for preprocessing imagery as applied toobject detection. We implemented the MDSR network forupscaling with a factor of 4, as well as a NN interpolationmethod, and used these upscaled images to train and validatemultiple models of the YOLOv2 object detection architec-ture. In addition, we proposed a novel pipeline that uses acombination of multi-stage tiling and upsampling with theYOLOv2 architecture to accurately identify vehicles withinlarge satellite scenes. In doing so, we add to the growing

literature of work in object detection in overhead imagery andsuper-resolution.

We demonstrated that multi-stage tiling and upscaling pro-duce significant improvements of very small objects withinlarge scenes, with images upscaled by a factor of 4 demon-strating a 22.8% improvement to the single-stage tiled imagesat the native GSD found in the xView dataset. However,we conclude that there are minimal differences between per-formance with object detectors trained using NN and SRmethods. Despite producing upscaled images that are closerto photo-realism than other interpolation algorithms, SR doesnot appear to yield any significant advantage when usedin conjunction with machine vision architectures for objectdetection. This calls into question the value of SR for practicalapplications and products given that SR has a significantlyhigher computational cost than NN.

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(a) (b) (c) (d)

Fig. 5: Object Detection Results on a Parking Lot Scene with column (a) showing a 1-stage tiling schema without upsampling,column (b) a 1-stage tiling with a NNx2 upscaling, column (c) demonstrating our proposed 2-stage tiling and upscaling pipelinewith MDSRx4, and column (d) 2-stage tiling with NNx4 upscaling

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