Saliency Analysis via Hyperparameter Sparse Representation and Energy Distribution Optimization for Remote Sensing Images

Zhang, Libao; Lv, Xinran; Xu, Liang

doi:10.3390/rs9060636

Cited by 6 publications

(6 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following the idea that salient object detection is a combination of region segmentation and saliency evaluation, [19] proposed a novel recursive clustering-based method and reported competitive results for multiple object detection. Moreover, there are several proposed methods based on sparse-low rank decomposition [43][44][45], which regard the salient regions as the sparse items, and methods based on sparse representation which made full use of the difference between the background and foreground [31,44,45].…”

Section: The Previous Saliency Detection Methodsmentioning

confidence: 99%

Salient Object Detection via Recursive Sparse Representation

et al. 2018

View full text Add to dashboard Cite

Object-level saliency detection is an attractive research field which is useful for many content-based computer vision and remote-sensing tasks. This paper introduces an efficient unsupervised approach to salient object detection from the perspective of recursive sparse representation. The reconstruction error determined by foreground and background dictionaries other than common local and global contrasts is used as the saliency indication, by which the shortcomings of the object integrity can be effectively improved. The proposed method consists of the following four steps: (1) regional feature extraction; (2) background and foreground dictionaries extraction according to the initial saliency map and image boundary constraints; (3) sparse representation and saliency measurement; and (4) recursive processing with a current saliency map updating the initial saliency map in step 2 and repeating step 3. This paper also presents the experimental results of the proposed method compared with seven state-of-the-art saliency detection methods using three benchmark datasets, as well as some satellite and unmanned aerial vehicle remote-sensing images, which confirmed that the proposed method was more effective than current methods and could achieve more favorable performance in the detection of multiple objects as well as maintaining the integrity of the object area.

show abstract

Section: The Previous Saliency Detection Methodsmentioning

confidence: 99%

Salient Object Detection via Recursive Sparse Representation

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The receiver operating characteristic (ROC) curve is derived by thresholding a saliency map at the threshold within the range [0, 255], and further classifying the saliency map into the saliency objects and the background [79]. The ROC graph is generated by plotting the true positive rate (on the y-axis) against the false positive rate (on the x-axis).…”

Section: Roc-auc Metricmentioning

confidence: 99%

An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images

2018

View full text Add to dashboard Cite

Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting learning saliency method for built-up area extraction from Sentinel-2 images is proposed. First, the optimal band combination for extracting such areas from Sentinel-2 data is determined; then, a coarse saliency map is generated, based on multiple cues and the geodesic weighted Bayesian (GWB) model, that provides training samples for a strong model; a refined saliency map is subsequently obtained using the strong model. Furthermore, cuboid cellular automata (CCA) is used to integrate multiscale saliency maps for improving the refined saliency map. Then, coarse and refined saliency maps are synthesized to create a final saliency map. Finally, the fractional-order Darwinian particle swarm optimization algorithm (FODPSO) is employed to extract the built-up areas from the final saliency result. Cities in five different types of ecosystems in China (desert, coastal, riverside, valley, and plain) are used to evaluate the proposed method. Analyses of results and comparative analyses with other methods suggest that the proposed method is robust, with good accuracy.

show abstract

“…Take the subbands LH 1 , HL 1 , and HH 1 , for example: the scanning order among blocks in LH 1 is conducted with the "vertical z-scan" method, and that in HL 1 follows the "horizontal z-scan" method. Because the energy of LH 1 is larger than that of HL 1 , the scanning order among blocks in HH 1 follows the "vertical z-scan" method. Finally, for a given block, its scanning method depends on the characteristics of the subband in which the block is located.…”

Section: Endmentioning

confidence: 99%

“…The remote sensing image "Europa3" is chosen as a test image. Suppose the decomposition level is 3, after the wavelet transform with DAL-PBT model, the scanning order among the subbands is determined according to the descending order of energy of these subbands, i.e., LL 3 , LH 3 , HH 3, HL 3, LH 2 , HH 2 , HL 2 , LH 1 , HL 1 , HH 1 . Following this, the scanning order among blocks in each subband is determined based on the characteristics of subbands.…”

Section: Endmentioning

confidence: 99%

“…Along with the rapid development of remote sensing technology, it is becoming easier to acquire high spatial resolution remote sensing images from various satellites and sensors, which is undoubtedly beneficial to the application of remote sensing images [1,2]. On the other hand, massive data has brought a great burden to the storage and transmission of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing Image Compression Based on Direction Lifting-Based Block Transform with Content-Driven Quadtree Coding Adaptively

et al. 2018

View full text Add to dashboard Cite

Due to the limitations of storage and transmission in remote sensing scenarios, lossy compression techniques have been commonly considered for remote sensing images. Inspired by the latest development in image coding techniques, we present in this paper a new compression framework, which combines the directional adaptive lifting partitioned block transform (DAL-PBT) with content-driven quadtree codec with optimized truncation (CQOT). First, the DAL-PBT model is designed; it calculates the optimal prediction directions of each image block and performs the weighted directional adaptive interpolation during the process of directional lifting. Secondly, the CQOT method is proposed, which provides different scanning orders among and within blocks based on image content, and encodes those blocks with a quadtree codec with optimized truncation. The two phases are closely related: the former is devoted to image representation for preserving more directional information of remote sensing images, and the latter leverages adaptive scanning on the transformed image blocks to further improve coding efficiency. The proposed method supports various progressive transmission modes. Experimental results show that the proposed method outperforms not only the mainstream compression methods, such as JPEG2000 and CCSDS, but also, in terms of some evaluation indexes, some state-of-the-art compression methods presented recently.

show abstract

Saliency Analysis via Hyperparameter Sparse Representation and Energy Distribution Optimization for Remote Sensing Images

Cited by 6 publications

References 51 publications

Salient Object Detection via Recursive Sparse Representation

Salient Object Detection via Recursive Sparse Representation

An Improved Boosting Learning Saliency Method for Built-Up Areas Extraction in Sentinel-2 Images

Remote Sensing Image Compression Based on Direction Lifting-Based Block Transform with Content-Driven Quadtree Coding Adaptively

Contact Info

Product

Resources

About