Boundary-semantic collaborative guidance network with dual-stream feedback mechanism for salient object detection in optical remote sensing imagery

With the increasing application of deep learning in various domains, salient object detection in optical remote sensing images (ORSI-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth, but fail to sufficiently optimize and protect the local information, and almost all approaches ignore the potential advantages offered by the last layer of the decoder to maintain the integrity of saliency maps. To address these issues, we propose a novel method named boundary-semantic collaborative guidance network (BSCGNet) with dual-stream feedback mechanism. First, we propose a boundary protection calibration (BPC) module, which effectively reduces the loss of edge position information during forward propagation and suppresses noise in low-level features without relying on boundary ground truth. Second, based on the BPC module, a dual feature feedback complementary (DFFC) module is proposed, which aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers, thereby enhancing cross-scale knowledge communication. Finally, to obtain more complete saliency maps, we consider the uniqueness of the last layer of the decoder for the first time and propose the adaptive feedback refinement (AFR) module, which further refines feature representation and eliminates differences between features through a unique feedback mechanism. Extensive experiments on three benchmark datasets demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent years. Codes and results have been released on GitHub: https://github.com/YUHsss/BSCGNet.Comment: Accepted by TGR

Multi-focus image fusion using maximum symmetric surround saliency detection

In digital photography, two or more objects of a scene cannot be focused at the same time. If we focus one object, we may lose information about other objects and vice versa. Multi-focus image fusion is a process of generating an all-in-focus image from several out-of-focus images. In this paper, we propose a new multi-focus image fusion method based on two-scale image decomposition and saliency detection using maximum symmetric surround. This method is very beneficial because the saliency map used in this method can highlight the saliency information present in the source images with well defined boundaries. A weight map construction method based on saliency information is developed in this paper. This weight map can identify the focus and defocus regions present in the image very well. So we implemented a new fusion algorithm based on weight map which integrate only focused region information into the fused image. Unlike multi-scale image fusion methods, in this method two-scale image decomposition is sufficient. So, it is computationally efficient. Proposed method is tested on several multi-focus image datasets and it is compared with traditional and recently proposed fusion methods using various fusion metrics. Results justify that our proposed method gives stable and promising performance when compared to that of the existing methods

Image and Information Fusion Experiments with a Software-Defined Multi-Spectral Imaging System for Aviation and Marine Sensor Networks

The availability of Internet, line-of-sight and satellite identification and surveillance information as well as low-power, low-cost embedded systems-on-a-chip and a wide range of visible to long-wave infrared cameras prompted Embry Riddle Aeronautical University to collaborate with the University of Alaska Arctic Domain Awareness Center (ADAC) in summer 2016 to prototype a camera system we call the SDMSI (Software-Defined Multi-spectral Imager). The concept for the camera system from the start has been to build a sensor node that is drop-in-place for simple roof, marine, pole-mount, or buoy-mounts. After several years of component testing, the integrated SDMSI is now being tested, first on a roof-mount at Embry Riddle Prescott. The roof-mount testing demonstrates simple installation for the high spatial, temporal and spectral resolution SDMSI. The goal is to define and develop software and systems technology to complement satellite remote sensing and human monitoring of key resources such as drones, aircraft and marine vessels in and around airports, roadways, marine ports and other critical infrastructure. The SDMSI was installed at Embry Riddle Prescott in fall 2016 and continuous recording of long-wave infrared and visible images have been assessed manually and compared to salient object detection to automatically record only frames containing objects of interest (e.g. aircraft and drones). It is imagined that ultimately users of the SDMSI can pair with it via wireless to browse salient images. Further, both ADS-B (Automatic Dependent Surveillance-Broadcast) and S-AIS (Satellite Automatic Identification System) data are envisioned to be used by the SDMSI to form expectations for observing in future tests. This paper presents the preliminary results of several experiments and compares human review with smart image processing in terms of the receiver-operator characteristic. The system design and software are open architecture, such that other researchers are encouraged to construct and participate in sharing results and networking identical or improved versions of the SDMSI for safety, security and drop-in-place scientific image sensor networking

A Global Human Settlement Layer from optical high resolution imagery - Concept and first results

A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen

Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Farm detection using low resolution satellite images is an important part of digital agriculture applications such as crop yield monitoring. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. In this paper, semantic segmentation of farm areas is addressed using low resolution satellite images. The segmentation is performed in two stages; First, local patches or Regions of Interest (ROI) that include farm areas are detected. Next, deep semantic segmentation strategies are employed to detect the farm pixels. For patch classification, two previously developed local patch classification strategies are employed; a two-step semi-supervised methodology using hand-crafted features and Support Vector Machine (SVM) modelling and transfer learning using the pretrained Convolutional Neural Networks (CNNs). For the latter, the high-level features learnt from the massive filter banks of deep Visual Geometry Group Network (VGG-16) are utilized. After classifying the image patches that contain farm areas, the DeepLabv3+ model is used for semantic segmentation of farm pixels. Four different pretrained networks, resnet18, resnet50, resnet101 and mobilenetv2, are used to transfer their learnt features for the new farm segmentation problem. The first step results show the superiority of the transfer learning compared to hand-crafted features for classification of patches. The second step results show that the model trained based on resnet50 achieved the highest semantic segmentation accuracy.acceptedVersionPeer reviewe

A Low-cost Depth Imaging Mobile Platform for Canola Phenotyping

To meet the high demand for supporting and accelerating progress in the breeding of novel traits, plant scientists and breeders have to measure a large number of plants and their characteristics accurately. A variety of imaging methodologies are being deployed to acquire data for quantitative studies of complex traits. When applied to a large number of plants such as canola plants, however, a complete three-dimensional (3D) model is time-consuming and expensive for high-throughput phenotyping with an enormous amount of data. In some contexts, a full rebuild of entire plants may not be necessary. In recent years, many 3D plan phenotyping techniques with high cost and large-scale facilities have been introduced to extract plant phenotypic traits, but these applications may be affected by limited research budgets and cross environments. This thesis proposed a low-cost depth and high-throughput phenotyping mobile platform to measure canola plant traits in cross environments. Methods included detecting and counting canola branches and seedpods, monitoring canola growth stages, and fusing color images to improve images resolution and achieve higher accuracy. Canola plant traits were examined in both controlled environment and field scenarios. These methodologies were enhanced by different imaging techniques. Results revealed that this phenotyping mobile platform can be used to investigate canola plant traits in cross environments with high accuracy. The results also show that algorithms for counting canola branches and seedpods enable crop researchers to analyze the relationship between canola genotypes and phenotypes and estimate crop yields. In addition to counting algorithms, fusing techniques can be helpful for plant breeders with more comfortable access plant characteristics by improving the definition and resolution of color images. These findings add value to the automation, low-cost depth and high-throughput phenotyping for canola plants. These findings also contribute a novel multi-focus image fusion that exhibits a competitive performance with outperforms some other state-of-the-art methods based on the visual saliency maps and gradient domain fast guided filter. This proposed platform and counting algorithms can be applied to not only canola plants but also other closely related species. The proposed fusing technique can be extended to other fields, such as remote sensing and medical image fusion

Using information content to select keypoints for UAV image matching

Image matching is one of the most important tasks in Unmanned Arial Vehicles (UAV) photogrammetry applications. The number and distribution of extracted keypoints play an essential role in the reliability and accuracy of image matching and orientation results. Conventional detectors generally produce too many redundant keypoints. In this paper, we study the effect of applying various information content criteria to keypoint selection tasks. For this reason, the quality measures of entropy, spatial saliency and texture coefficient are used to select keypoints extracted using SIFT, SURF, MSER and BRISK operators. Experiments are conducted using several synthetic and real UAV image pairs. Results show that the keypoint selection methods perform differently based on the applied detector and scene type, but in most cases, the precision of the matching results is improved by an average of 15%. In general, it can be said that applying proper keypoint selection techniques can improve the accuracy and efficiency of UAV image matching and orientation results. In addition to the evaluation, a new hybrid keypoint selection is proposed that combines all of the information content criteria discussed in this paper. This new screening method was also compared with those of SIFT, which showed 22% to 40% improvement for the bundle adjustment of UAV images