Optimum graph cuts for pruning binary partition trees of polarimetric SAR images

This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed.Peer ReviewedPostprint (author's final draft

Exploitation of the additive component of the polarimetric noise model for speckle filtering

Ratio filters for speckle noise reduction in SAR imagery are recursive filters where the image structure is iteratively recovered from an initial oversmoothed image. We show that the MBPolSAR filter could be interepreted as a ratio filter applied to the off-diagonal terms of the covariance/coherency matix. From this observation, we propose a new polarimetric ratio filter allowing us to recover the image structure from all the terms of the covariance matrix. In addition, we briefly look at how the additive noise component could also be exploited for the image structure extraction. Filtering results on both simulated and real PolSAR images are shown.Peer ReviewedPostprint (published version

Towards the automation of large mammal aerial survey in Africa

editorial reviewedIn African open protected areas, large mammals are often surveyed using manned aircrafts which actively count the animals in sample strips for later density extrapolation to the whole area. Nevertheless, this method may be biased among others by the observer’s detection capability. The use of on-board oblique cameras has recently shown an increase in counting accuracy as a result of indirect photo-interpretation. While this approach appears to reduce some biases, the processing time of the generated data is currently a bottleneck. In recent years, Deep Learning (DL) techniques through dense convolutional neural networks (CNNs) have emerged as a very promising avenue for managing such datasets. However, we are not yet at the stage of full automation of the process (i.e. from acquisition to population estimation). Three challenges were identified: 1) reducing false positives, 2) increasing the precision in close-by individuals, and 3) properly managing the overlap between images to avoid double counting. We focused on the two first aspects and developed a new point-based DL model inspired by crowd counting, that was applied on a challenging oblique aerial dataset containing free ranging livestock herds in heterogeneous open arid landscapes. The model’s performances were then evaluated using localization and counting metrics. The DL model achieved a global F1 score of 0.74 and a RMSE of 9.8 animals per 24 megapixel image, at a processing speed of 3.6 s/image. It showed a valuable ability to detect both isolated animals and those in dense herds. This is auspicious for automation of African mammal surveys but the developed approach still needs to be improved to manage double counting on entire transects. These results emphasize the importance of standardization of data acquisition, with strong spatial and temporal heterogeneities, in order to build robust models that can be used in similar environments and conditions

Counting African Mammal Herds in Aerial Imagery Using Deep Learning: Are Anchor-Based Algorithms the Most Suitable?

editorial reviewedMonitoring wildlife and livestock in protected areas is essential to reach natural ecosystem conservation goals. In large open areas, this is often carried out by direct counting from observers in manned aircrafts flying at low altitude. However, there are several biases associated with this method, resulting in a low accuracy of large groups counts. Unmanned Aerial Vehicles (UAVs) have experienced a significant growth in recent years and seem to be relatively well-suited systems for photographing animals. While UAVs allow for more accurate herd counts than traditional methods, identification and counting are usually indirectly done during a manual time-consuming photo-interpretation process. For several years, machine learning and deep learning techniques have been developed and now show encouraging results for automatic animal detection. Some of them use Convolutional Neural Networks (CNNs) through anchor-based object detectors. These algorithms automatically extract relevant features from images, produce thousands of anchors all over the image and eventually decide which ones actually contain an object. Counting and classification are then achieved by summing and classifying all the selected bounding boxes. While this approach worked well for isolated mammals or sparse herds, it showed limits in close-by individuals by generating too many false positives, resulting in overestimated counts in dense herds. This raises the question: are anchor-based algorithms the most suitable for counting large mammals in aerial imagery? In an attempt to answer this, we built a simple one stage point-based object detector on a dataset acquired over various African landscapes which contains six large mammal species: buffalo (Syncerus caffer), elephant (Loxodonta africana), kob (Kobus kob), topi (Damaliscus lunatus jimela), warthog (Phacochoerus africanus) and waterbuck (Kobus ellipsiprymnus). An adapted version of the CNN DLA-34 was trained on points only (center of the original bounding boxes), splat onto a Focal Inverse Distance Transform (FIDT) map regressed in a pixel-wise manner using the focal loss. During inference, local maxima were extracted from the predicted map to obtain the animals location. Binary model’s performances were then compared to those of the state-of-the-art model, Libra-RCNN. Although our model detected 5% fewer animals compared to the baseline, its precision doubled from 37% to 70%, reducing the number of false positives by one third without using any hard negative mining method. The results obtained also showed a clear increase in precision in close-by individuals areas, letting it appear that a point-based approach seems to be better adapted for animal detection in herds than anchor-based ones. Future work will apply this approach on other animal datasets with different acquisition conditions (e.g. oblique viewing angle, coarser resolution, denser herds) to evaluate its range of use

From crowd to herd counting: How to precisely detect and count African mammals using aerial imagery and deep learning?

peer reviewedRapid growth of human populations in sub-Saharan Africa has led to a simultaneous increase in the number of livestock, often leading to conflicts of use with wildlife in protected areas. To minimize these conflicts, and to meet both communities’ and conservation goals, it is therefore essential to monitor livestock density and their land use. This is usually done by conducting aerial surveys during which aerial images are taken for later counting. Although this approach appears to reduce counting bias, the manual processing of images is timeconsuming. The use of dense convolutional neural networks (CNNs) has emerged as a very promising avenue for processing such datasets. However, typical CNN architectures have detection limits for dense herds and closeby animals. To tackle this problem, this study introduces a new point-based CNN architecture, HerdNet, inspired by crowd counting. It was optimized on challenging oblique aerial images containing herds of camels (Camelus dromedarius), donkeys (Equus asinus), sheep (Ovis aries) and goats (Capra hircus), acquired over heterogeneous arid landscapes of the Ennedi reserve (Chad). This approach was compared to an anchor-based architecture, Faster-RCNN, and a density-based, adapted version of DLA-34 that is typically used in crowd counting. HerdNet achieved a global F1 score of 73.6 % on 24 megapixels images, with a root mean square error of 9.8 animals and at a processing speed of 3.6 s, outperforming the two baselines in terms of localization, counting and speed. It showed better proximity-invariant precision while maintaining equivalent recall to that of Faster-RCNN, thus demonstrating that it is the most suitable approach for detecting and counting large mammals at close range. The only limitation of HerdNet was the slightly weaker identification of species, with an average confusion rate approximately 4 % higher than that of Faster-RCNN. This study provides a new CNN architecture that could be used to develop an automatic livestock counting tool in aerial imagery. The reduced image analysis time could motivate more frequent flights, thus allowing a much finer monitoring of livestock and their land use

Flexible Glass Hybridized Colloidal Quantum Dots for Gb/s Visible Light Communications

Color converting films of colloidal quantum dots (CQDs) encapsulated with flexible glass are integrated with microsize GaN LEDs (μLEDs) in order to form optical sources for high-speed visible light communications (VLC). VLC is an emerging technology that uses white and/or colored light from LEDs to combine illumination and display functions with the transmission of data. The flexible glass/CQD format addresses the issue of limited modulation speed of typical phosphor-converted LEDs while enhancing the photostability of the color converters and facilitating their integration with the μLEDs. These structures are less than 70 μm in total thickness and are directly placed in contact with the polished sapphire substrate of 450-nm-emitting μLEDs. Blue-to-green, blue-to-orange and blue-to-red conversion with respective forward optical power conversion efficiencies of 13%, 12% and 5.5% are reported. In turn, free-space optical communications up to 1.4 Gb/s VLC is demonstrated. Results show that CQD-converted LEDs pave the way for practical digital lighting/displays with multi-Gb/s capability