Detection of Buildings in Multispectral Very High Spatial Resolution Images Using the Percentage Occupancy Hit-or-Miss Transform

Stankov, Katia; He, Dong-Chen

doi:10.1109/jstars.2014.2308301

Cited by 49 publications

(39 citation statements)

References 32 publications

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“…R-CNN based methods (such as R-CNN, Fast R-CNN and Faster R-CNN) choose the output of the last layer as reference set of feature maps [3][4][5]. However, the single scale feature maps have a fixed receptive field of the input image, which can be mismatched to small or large objects [47].…”

Section: Shared Multi-scale Base Networkmentioning

confidence: 99%

“…Automated object detection in HRS images is a core requirement for large range scene understanding and semantic information extraction [2]. Over the past decades, considerable efforts have been made to develop various methods for the detection of different types of objects in satellite and aerial images [3], such as buildings [4,5], storage tanks [6,7], vehicles [8,9], and airplanes [10][11][12]. Object detection in HRS images determines whether there are one or more objects belonging to the classes we are looking for and locates the position of each object using a bounding box.…”

Section: Introductionmentioning

confidence: 99%

“…Such types of methods usually have Remote Sens. 2018, 10, 131 3 of 21 two steps: template generation and similarity measure [4,24]. For knowledge-based methods, the most widely leveraged types of knowledge are geometric and context [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network

et al. 2018

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Daily acquisition of large amounts of aerial and satellite images has facilitated subsequent automatic interpretations of these images. One such interpretation is object detection. Despite the great progress made in this domain, the detection of multi-scale objects, especially small objects in high resolution satellite (HRS) images, has not been adequately explored. As a result, the detection performance turns out to be poor. To address this problem, we first propose a unified multi-scale convolutional neural network (CNN) for geospatial object detection in HRS images. It consists of a multi-scale object proposal network and a multi-scale object detection network, both of which share a multi-scale base network. The base network can produce feature maps with different receptive fields to be responsible for objects with different scales. Then, we use the multi-scale object proposal network to generate high quality object proposals from the feature maps. Finally, we use these object proposals with the multi-scale object detection network to train a good object detector. Comprehensive evaluations on a publicly available remote sensing object detection dataset and comparisons with several state-of-the-art approaches demonstrate the effectiveness of the presented method. The proposed method achieves the best mean average precision (mAP) value of 89.6%, runs at 10 frames per second (FPS) on a GTX 1080Ti GPU.

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Section: Shared Multi-scale Base Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network

et al. 2018

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“…Convolution structure can reduce the amount of memory occupied by deep network, and also reduce the number of network parameters, alleviate the over fitting problem of the model. In 2012, Krizhevskywin the ImageNet competition image classification task by the use of a CNN called AlexNet [23], which illustrate the prominent advantage of CNN in image understanding [24][25][26][27][28][29][30][31]. Especially since Zeiler [32] put forward the deconvolution network model, people subsequently developed FCN [33][34][35], UNET [36,37], Deeplab [38,39], which greatly improved the accuracy of image segmentation.…”

Section: Introducementioning

confidence: 99%

A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks

Yang¹,

Zhang²,

Gao³

et al. 2018

Preprint

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When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BCNET) model base on standard fully convolutional networks(FCN) to solve these problems. First take building with no shade or artificial removal of shade as Sample-A, woodland as Sample-B, road as Sample-C. Set up 3 sample libraries. Learn these sample libraries respectively, get their own set of feature vector; Mixture Gauss model these feature vector set, evaluate the conditional probability density function of mixture of noise object and roofs; Improve the standard FCN from the 2 aspect:(1) Introduce atrous convolution. (2) Take conditional probability density function as the activation function of the last convolution. Carry out experiment using unmanned aerial vehicle(UVA) image, the results show that BCNET model can effectively eliminate the influence of trees and roads, the building extraction accuracy can reach 97%. Keywords:High resolution remote sensing image; Convolutional Neural Networks; Full Convolution Networks; Bayesian Convolutional Neural Networks; building extraction; conditional probability density function. 1.IntroduceIt is of great significance to obtain the detailed distribution map of urban buildings in the social management and development plan. High resolution remote sensing images contain lots of object features, including color, size, shape, texture and layout of the relationship between objects, which makes it possible to accurately extract buildings from high resolution remote sensing images [1]. The absence of low-cost high resolution multi spectral image with meter/sub-meter accuracy provided reliable source of data for the building extraction. In remote sensing image, the outline of the building is characterized mainly by the shape and distribution of the roof [2]. Because the material, color, shape, size and orientation of the roofs are diverse, and some of the roofs even shaded by trees or other tall buildings, which makes it difficult to extract building from high resolution multi spectral remote sensing image.Because the urban environment is complicated, the spatial pattern of buildings is relatively complex, object has high spectral variability in remote sensing image. same objects with different spectrum is common in the urban remote sensing image. Thus it is difficult for auto/semi-auto classification base on pixel spectral signature [3]. Studies have shown that although the 4 multi spectral bands of high resolution remote sensing image can be able to distinguish the land cover types of town's water, bare soil, vegetation, shadow and impervious ground [4,5]. But only with these low level spectral statistical characteristics, it is hard to extract road, parking and building from impervious ground [6]. In recent years, methods combining spatial and spectral analysis have been put forward. It is considered that spatial cha...

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“…There has been a considerate amount of research in optical imagery understanding focusing on detection of different types of objects, such as roads [3,4], buildings [5,6], oil tanks [7,8], vehicles [9][10][11] and airplanes [12][13][14]. Aside from detecting scattered objects, the classification of scenes also receives a lot of attention recently, such as in [15][16][17], where the objective is to classify image patches into different classes, such as buildings, forest, harbor, etc.…”

Section: Introductionmentioning

confidence: 99%

Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

2017

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Abstract:In current remote sensing literature, the problems of sea-land segmentation and ship detection (including in-dock ships) are investigated separately despite the high correlation between them. This inhibits joint optimization and makes the implementation of the methods highly complicated. In this paper, we propose a novel fully convolutional network to accomplish the two tasks simultaneously, in a semantic labeling fashion, i.e., to label every pixel of the image into 3 classes, sea, land and ships. A multi-scale structure for the network is proposed to address the huge scale gap between different classes of targets, i.e., sea/land and ships. Conventional multi-scale structure utilizes shortcuts to connect low level, fine scale feature maps to high level ones to increase the network's ability to produce finer results. In contrast, our proposed multi-scale structure focuses on increasing the receptive field of the network while maintaining the ability towards fine scale details. The multi-scale convolution network accommodates the huge scale difference between sea-land and ships and provides comprehensive features, and is able to accomplish the tasks in an end-to-end manner that is easy for implementation and feasible for joint optimization. In the network, the input forks into fine-scale and coarse-scale paths, which share the same convolution layers to minimize network parameter increase, and then are joined together to produce the final result. The experiments show that the network tackles the semantic labeling problem with improved performance.

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Detection of Buildings in Multispectral Very High Spatial Resolution Images Using the Percentage Occupancy Hit-or-Miss Transform

Cited by 49 publications

References 32 publications

Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network

Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network

A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks

Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

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