Remote sensing applications have gained in popularity in recent years, which has resulted in vast amounts of data being produced on a daily basis. Managing and delivering large sets of data becomes extremely difficult and resource demanding for the data vendors, but even more for individual users and third party stakeholders. Hence, research in the field of efficient remote sensing data handling and manipulation has become a very active research topic (from both storage and communication perspectives). Driven by the rapid growth in the volume of optical satellite measurements, in this work we explore the lossy compression technique for multispectral satellite images. We give a comprehensive analysis of the High Efficiency Video Coding (HEVC) still-image intra coding part applied to the multispectral image data. Thereafter, we analyze the impact of the distortions introduced by the HEVC’s intra compression in the general case, as well as in the specific context of crop classification application. Results show that HEVC’s intra coding achieves better trade-off between compression gain and image quality, as compared to standard JPEG 2000 solution. On the other hand, this also reflects in the better performance of the designed pixel-based classifier in the analyzed crop classification task. We show that HEVC can obtain up to 150:1 compression ratio, when observing compression in the context of specific application, without significantly losing on classification performance compared to classifier trained and applied on raw data. In comparison, in order to maintain the same performance, JPEG 2000 allows compression ratio up to 70:1.

CycleGAN domain transfer architectures use cycle consistency loss mechanisms to enforce the bijectivity of highly underconstrained domain transfer mapping. In this paper, in order to further constrain the mapping problem and reinforce the cycle consistency between two domains, we also introduce a novel regularization method based on the alignment of feature maps probability distributions. This type of optimization constraint, expressed via an additional loss function, allows for further reducing the size of the regions that are mapped from the source domain into the same image in the target domain, which leads to mapping closer to the bijective and thus better performance. By selecting feature maps of the network layers with the same depth d in the encoder of the direct generative adversarial networks (GANs), and the decoder of the inverse GAN, it is possible to describe their d-dimensional probability distributions and, through novel regularization term, enforce similarity between representations of the same image in both domains during the mapping cycle. We introduce several ground distances between Gaussian distributions of the corresponding feature maps used in the regularization. In the experiments conducted on several real datasets, we achieved better performance in the unsupervised image transfer task in comparison to the baseline CycleGAN, and obtained results that were much closer to the fully supervised pix2pix method for all used datasets. The PSNR measure of the proposed method was, on average, 4.7% closer to the results of the pix2pix method in comparison to the baseline CycleGAN over all datasets. This also held for SSIM, where the described percentage was 8.3% on average over all datasets.

Practical applications of computer vision in smart cities usually assume system integration and operation in challenging open-world environments. In the case of person re-identification task the main goal is to retrieve information whether the specific person has appeared in another place at a different time instance of the same video, or over multiple camera feeds. This typically assumes collecting raw data from video surveillance cameras in different places and under varying illumination conditions. In the considered open-world setting it also requires detection and localization of the person inside the analyzed video frame before the main re-identification step. With multi-person and multi-camera setups the system complexity becomes higher, requiring sophisticated tracking solutions and re-identification models. In this work we will discuss existing challenges in system design architectures, consider possible solutions based on different computer vision techniques, and describe applications of such systems in retail stores and public spaces for improved marketing analytics. In order to analyse sensitivity of person re-identification task under different open-world environments, a performance of one close to real-time solution will be demonstrated over several video captures and live camera feeds. Finally, based on conducted experiments we will indicate further research directions and possible system improvements.

Due to the large-scale disappearance of grasslands there is an urgent need for revitalization. It calls for consistent and accessible monitoring and mapping plans, and an integrated management approach. However, revitalization efforts often focus solely on the vegetation component, and skip the link to other animal species that perform vital functions as ecosystem engineers and umbrella species. In this study, we combine an in-situ standard phytocoenological survey with an UAV-based technology in the effort to improve the monitoring and mapping of the sandy steppe habitat of the European ground squirrel (Spermophilus citellus; EGS), undergoing revitalization in the northern Serbia. It is a model organism of an animal species that enables identifying habitat quality and quantity indicators to understand the broader implications of the ecosystem revitalization efforts on the wildlife populations. The proposed approach tested whether the commercially available RGB sensor and a relatively high flight height of the UAV have discriminative capacity to aid site managers by mapping identified steppe development stages (specific plant assemblages, reflecting different habitat types). Thus, a novel set of high-resolution image descriptors that are capable of discriminating plant mixtures corresponding to Fallow land, Forest steppe and shrubs, Young steppe I and II, was proposed. Despite high resolution imaging, the method solves a challenging problem of UAV vegetation mapping in the case of limited spectral and spatial information in the image (by using only RGB camera and multitemporal approach). Although the lack of visual information that would allow identification of individual plant parts and shapes prevented the use of usual object-based image analysis, proposed pixel-based descriptors and feature selection were able to provide the extent of the targeted areas and their compositional carriers. Presented holistic approach enables implementation of effective management strategies that support the entire ecological community.

Most CycleGAN domain transfer architectures require a large amount of data belonging to domains on which the domain transfer task is to be applied. Nevertheless, in many real-world applications one of the domains is reduced, i.e., scarce. This means that it has much less training data available in comparison to the other domain, which is fully observable. In order to tackle the problem of using CycleGAN framework in such unfavorable application scenarios, we propose and invoke a novel Bootstrapped SSL CycleGAN architecture (BTS-SSL), where the mentioned problem is overcome using two strategies. Firstly, by using a relatively small percentage of available labelled training data from the reduced or scarce domain and a Semi-Supervised Learning (SSL) approach, we prevent overfitting of the discriminator belonging to the reduced domain, which would otherwise occur during initial training iterations due to the small amount of available training data in the scarce domain. Secondly, after initial learning guided by the described SSL strategy, additional bootstrapping (BTS) of the reduced data domain is performed by inserting artifically generated training examples into the training poll of the data discriminator belonging to the scarce domain. Bootstrapped samples are generated by the already trained neural network that performs transferring from the fully observable to the scarce domain. The described procedure is periodically repeated during the training process several times and results in significantly improved performance of the final model in comparison to the original unsupervised CycleGAN approach. The same also holds in comparison to the solutions that are exclusively based either on the described SSL, or on the bootstrapping strategy, i.e., when these are applied separately. Moreover, in the considered scarce scenarios it also shows competitive results in comparison to the fully supervised solution based on the pix2pix method. In that sense, it is directly applicable to many domain transfer tasks that are relying on the CycleGAN architecture.