Remote sensing data can be utilized to help developing countries monitor the use of land. However, the problem of constant cloud coverage prevents us from taking full advantage of satellite optical images. Therefore, we instead opt to use data from synthetic-aperture radar (SAR), which can capture images of the Earth's surface regardless of the weather conditions. In this study, we use SAR data to identify newly built constructions. Most studies on change detection tend to detect all of the changes that have a similar temporal change characteristic occurring on two occasions, while we want to identify only the constructions and avoid detecting other changes such as the seasonal change of vegetation. To do so, we study various deep learning network techniques and have decided to propose the fully convolutional network with a skip connection. We train this network with pairs of SAR data acquired on two different occasions from Bangkok and the ground truth, which we manually create from optical images available from Google Earth for all of the SAR pairs. Experiments to assign the most suitable patch size, loss weighting, and epoch number to the network are discussed in this paper. The trained model can be used to generate a binary map that indicates the position of these newly built constructions precisely with the Bangkok dataset, as well as with the Hanoi and Xiamen datasets with acceptable results. The proposed model can even be used with SAR images of the same specific satellite from another orbit direction and still give promising results. identify one specific change, such as the appearance of new buildings, as any kind of change similar to the target change would be involved in the results. For instance, Y. Ban and O. Yousif [6] used the threshold-based method on a difference image in detecting urban change. Despite the good detection result, there is a possibility to detect falsely when the urban or non-urban area has unordinary intensity change behavior. In this research, our target is to monitor the increase in new construction in urban and suburban areas. To complete this objective, we used a deep learning technique to identify these newly built constructions from two SAR images directly without generating a difference image. The goal of deep learning is to simulate the experience-based learning mechanism of the human brain using training data and ground truth data in the same way that humans learn [7]. To date, deep learning has been highly effective, especially in the image processing field. One of the most successful deep learning networks that we considered using in this work is the U-net [8]. The U-net, proposed in 2016 for the purpose of medical image segmentation, was built on the basis of adding a skip connection to the fully convolutional network (FCN) [9] between the encoder part and decoder part. With the skip connection, the decoders can receive a low-level feature from the encoder and form the output without losing boundary information in the process. Because of its precisely predicted output...

In this paper, we propose a level-set method to identify urban areas using a nighttime light data of Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). Our method is compared to two standard methods, called the Otsu's threshold-based method and the k-mean clustering method. The experimental results indicates significant improvement in terms of the Kappa coefficients.

No abstract

The limitations in obtaining sufficient datasets for training deep learning networks is preventing many applications from achieving accurate results, especially when detecting new constructions using time-series satellite imagery, since this requires at least two images of the same scene and it must contain new constructions in it. To tackle this problem, we introduce Chronological Order Reverse Network (CORN)-an architecture for detecting newly built constructions in time-series SAR images that does not require a large quantity of training data. The network uses two U-net adaptations to learn the changes between images from both Time 1-Time 2 and Time 2-Time 1 formats, which allows it to learn double the amount of changes in different perspectives. We trained the network with 2028 pairs of 256 × 256 pixel SAR images from ALOS-PALSAR, totaling 4056 pairs for the network to learn from, since it learns from both Time 1-Time 2 and Time 2-Time 1. As a result, the network can detect new constructions more accurately, especially at the building boundary, compared to the original U-net trained by the same amount of training data. The experiment also shows that the model trained with CORN can be used with images from Sentinel-1. The source code is available at https://github.com/Raveerat-titech/CORN. Remote Sens. 2020, 12, 990 2 of 25 Remote Sens. 2020, 12, 990 3 of 25 the same ground truth data. While normally, the detection of new buildings is supposed to use the data in Time 1-Time 2 format, our proposed architecture takes both Time 1-Time 2 and Time 2-Time 1 formats of data to allow learning based on both of the changing features to make it more viable. With this architecture, the amount of training data of the network appears to be doubled. This allows the network to be trained with a greater variation of data, and can result an increased detection accuracy without having to use more SAR data or create any additional ground truths. Moreover, CORN has the potential to use SAR images from other satellites and other environments because the training back and forth causes the model to be more robust.In summary, the objective of this paper is to cope with the lack of training data when training deep learning networks, which leads to inaccurate results in newly built construction detection. We do so by proposing a network architecture called "CORN", which doubles the training set by reversing the chronological order of the dataset. CORN contains two U-net adaptations; one trains on Time 1-Time 2 images, and the other one trains on Time 2-Time 1 images. The proposed network not only increases the detection accuracy, but can also be used in a greater variety of settings of the data; specifically, images from other satellites and other acquisition conditions, including the terrain of the testing area.

No abstract