Deep convolutional networks perform well in remote sensing (RS) image classification. Usually, it is difficult to obtain a large number of labeled samples in remote sensing classification tasks. Traditionally, the acquisition of remote sensing images is quite different from the photos provided by digital cameras. However, the imaging system for high resolution (HR) RS images (often with RGB 3 channels) is similar to those provided by digital cameras. In the paper, a transfer learning algorithm based on deep neural networks is proposed to attack the problem of lacking labeled RS samples, in particular on the context of pre-trained deep convolutional networks, i.e., VGGNet. Here, the VGGNet is trained on labeled multimedia images provided by “ImageNet Large Scale Visual Recognition Challenge” (ILSVRC). In the proposed strategy, the VGGNet is adopted as a base classifier, and then labeled RS data samples are exploited to fine-tune higher hidden layers in the 16-layer VGG deep neural networks by the back-propagation algorithm. The proposed method is denoted as RS-VGGNet. The proposed RS-VGGNet is validated by real HR remote sensing images, which were acquired from the National Agriculture Imagery Program(NAIP) dataset. Experimental results show that the RS-VGGNet can achieve a higher accuracy compared to the original VGGNet and shallow machine learning methods. And the proposed RS-VGGNet significantly reduces training times and computing burden as well.

In recent decades, it is easy to obtain remote sensing images which have been successfully applied to various applications, such as urban planning, hazard monitoring, etc. In particular, high resolution (HR) remote sensing (RS) images can better monitor our living environment from a broader spatial perspective. However, raw remote sensing images provide no labeling information to train a classifier, which usually is exploited to generate remote sensing maps. Based on our previous work, in the paper, an automatic classification system is proposed to classify high resolution urban RS images using deep neural networks, in particular, convolutional neural networks and fully convolutional networks. The labeling information is assigned on the context of both social media photos and HR remote sensing images by significantly reducing the cost of manual labeling without the necessity of remote sensing experts. The experiments carried out on high resolution remote sensing images acquired in the city Frankfurt taken by the Jilin-1 satellites confirm the effectiveness of the proposed strategy compared to the state of the art.

With the rapid development of the internet and popularization of intelligent mobile devices, social media is evolving fast and contains rich spatial information, such as geolocated posts, tweets, photos, video, and audio. Those location-based social media data have offered new opportunities for hazards and disaster identification or tracking, recommendations for locations, friends or tags, pay-per-click advertising, etc. Meanwhile, a massive amount of remote sensing (RS) data can be easily acquired in both high temporal and spatial resolution with a multiple satellite system, if RS maps can be provided, to possibly enable the monitoring of our location-based living environments with some devices like charge-coupled device (CCD) cameras but on a much larger scale. To generate the classification maps, usually, labeled RS image pixels should be provided by RS experts to train a classification system. Traditionally, labeled samples are obtained according to ground surveys, image photo interpretation or a combination of the aforementioned strategies. All the strategies should be taken care of by domain experts, in a means which is costly, time consuming, and sometimes of a low quality due to reasons such as photo interpretation based on RS images only. These practices and constraints make it more challenging to classify land-cover RS images using big RS data. In this paper, a new methodology is proposed to classify urban RS images by exploiting the semantics of location-based social media photos (SMPs). To validate the effectiveness of this methodology, an automatic classification system is developed based on RS images as well as SMPs via big data analysis techniques including active learning, crowdsourcing, shallow machine learning, and deep learning. As the labels of RS training data are given by ordinary people with a crowdsourcing technique, the developed system is named Crowd4RS. The quantitative and qualitative experiments confirm the effectiveness of the proposed Crowd4RS system as well as the proposed methodology for automatically generating RS image maps in terms of classification results based on big RS data made up of multispectral RS images in a high spatial resolution and a large amount of photos from social media sites, such as Flickr and Panoramio.