A Scalable System for Searching Large-scale Multi-sensor Remote Sensing Image Collections

2021 ◽  
Author(s):  
Yifan Zhao ◽  
Xian Yang ◽  
Ranga Raju Vatsavai
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Scalable System

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

2021 ◽  
Vol 13 (8) ◽  
pp. 1563
Author(s):  
Yuanyuan Tao ◽  
Qianxin Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Data Availability ◽  
Function Evaluation ◽  
Characteristic Analysis ◽  
Accurate Identification ◽  
Living Space ◽  
Evolution Characteristics

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals A review of parallel computing for large-scale remote sensing image mosaicking

2015 ◽  
Vol 18 (2) ◽  
pp. 517-529 ◽  
Author(s):  
Lajiao Chen ◽  
Yan Ma ◽  
Peng Liu ◽  
Jingbo Wei ◽  
Wei Jie ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Parallel Computing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Image Mosaicking

scholarly journals RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1594
Author(s):  
Haifeng Li ◽  
Xin Dou ◽  
Chao Tao ◽  
Zhixiang Wu ◽  
Jie Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Large Scale ◽  
Remote Sensing Image ◽  
National Standard ◽  
Natural Image ◽  
Deep Convolutional Neural Networks ◽  
Remote Sensing Image Classification ◽  
Crowdsourced Data ◽  
Classification Tasks

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.

High-Throughput, Resource-Efficient Multi-Dimensional Parallel Architecture for Space-Borne Sea-Land Segmentation

2020 ◽  
pp. 2150027
Author(s):  
Cunguang Zhang ◽  
Hongxun Jiang ◽  
Riwei Pan ◽  
Haiheng Cao ◽  
Mingliang Zhou
Keyword(s):  
Remote Sensing ◽  
High Throughput ◽  
Large Scale ◽  
Parallel Architecture ◽  
Remote Sensing Image ◽  
Satellite System ◽  
Peak Performance ◽  
Remote Sensing Images ◽  
Block Level ◽  
Remote Sensing Image Processing

Sea-land segmentation based on edge detection is commonly utilized in ship detection, coastline extraction, and satellite system applications due to its high accuracy and rapid speed. Pixel-level distribution statistics do not currently satisfy the requirements for high-resolution, large-scale remote sensing image processing. To address the above problem, in this paper, we propose a high-throughput hardware architecture for sea-land segmentation based on multi-dimensional parallel characteristics. The proposed architecture is well suited to wide remote sensing images. Efficient multi-dimensional block level statistics allow for relatively infrequent pixel-level memory access; a boundary block tracking process replaces the whole-image scanning process, markedly enhancing efficiency. The tracking efficiency is further improved by a convenient two-step scanning strategy that feeds back the path state in a timely manner for a large number of blocks in the same direction appearing in the algorithm. The proposed architecture was deployed on Xilinx Virtex k7-410t to find that its practical processing time for a [Formula: see text] remote sensing image is only about 0.4[Formula: see text]s. The peak performance is 1.625[Formula: see text]gbps, which is higher than other FPGA implementations of segmentation algorithms. The proposed structure is highly competitive in processing wide remote sensing images.

scholarly journals Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

2019 ◽  
Vol 11 (17) ◽  
pp. 2055 ◽  
Author(s):  
Xu Tang ◽  
Chao Liu ◽  
Jingjing Ma ◽  
Xiangrong Zhang ◽  
Fang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Content Organization ◽  
Hash Code ◽  
Improved Performance ◽  
Hashing Function ◽  
Image Archives ◽  
Class Variable

Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers’ attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance.

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