A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image. Being different from the traditional convolutional network with only mono-scale pooling kernels, in the proposed method, multi-scale pooling kernels are equipped in a convolutional network to exploit the spatial context information on changed regions from the difference image. Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset. We compare the proposed method with other state-of-arts and the comparisons are performed on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that our proposed method obtains comparable results with S-PCA-Net on YR-A and YR-B dataset and outperforms other state-of-art methods, especially on the Sendai-A and Sendai-B datasets with more complex scenes. More important, MSSP-Net is more efficient than S-PCA-Net and convolutional neural networks (CNN) with less executing time in both training and testing phases

Application of Convolutional Neural Network in the Segmentation and Classification of High-Resolution Remote Sensing Images

Numerous convolution neural networks increase accuracy of classification for remote sensing scene images at the expense of the models space and time sophistication This causes the model to run slowly and prevents the realization of a trade-off among model accuracy and running time The loss of deep characteristics as the network gets deeper makes it impossible to retrieve the key aspects with a sample double branching structure which is bad for classifying remote sensing scene photo

Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network

Change detection from synthetic aperture radar images becomes a key technique to detect change area related to some phenomenon as flood and deformation of the earth surface. This paper proposes a transfer learning and Residual Network with 18 layers (ResNet-18) architecture-based method for change detection from two synthetic aperture radar images. Before the application of the proposed technique, batch denoising using convolutional neural network is applied to the two input synthetic aperture radar image for speckle noise reduction. To validate the performance of the proposed method, three known synthetic aperture radar datasets (Ottawa; Mexican and for Taiwan Shimen datasets) are exploited in this paper. The use of these datasets is important because the ground truth is known, and this can be considered as the use of numerical simulation. The detected change image obtained by the proposed method is compared using two image metrics. The first metric is image quality index that measures the similarity ratio between the obtained image and the image of the ground truth, the second metrics is edge preservation index, it measures the performance of the method to preserve edges. Finally, the method is applied to determine the changed area using two Sentinel 1 B synthetic aperture radar images of Eddahbi dam situated in Morocco

Comparison of Supervised Image Classification Algorithms: Classifying Diverse Land Cover in California

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsThe research field of machine learning and supervised image classification is quickly developing. There are many studies regarding the different use cases of image classification. However, a comprehensive study on the primary algorithms in ArcGIS Pro has not been assessed for numerous classes. This study attempts to bridge that gap by evaluating the effectiveness of the three primary classification algorithms available in ArcGIS Pro, and to determine an optimal algorithm for the given study area. This scope covers 12 classes of land cover in San Joaquin County, California. Maximum Likelihood, Random Forest, and Support Vector Machine were tested based on their general usability in image classification as well as their proven characteristics through research. The training and ground truth validation data were provided by USGS, in the form of a Landsat 8 image, and crop planning map. The accuracy assessment was performed with a stratified random sampling strategy. Based on the Kappa statistic, this study determines Random Forest (Kappa = 0.68, Accuracy = 0.76) to be the most suitable algorithm for detecting a series of crop types, bodies of water, and urban spaces apart from the rest of the land cover in San Joaquin County, California, USA. In addition to determining a preferred algorithm, it is also apparent that certain parameters when tweaked, produce the optimal classifier for this dataset. In this case, this means most parameters set to default, with an increased spectral detail and a decreased spatial detail. What this indicates for crop planning is that the current algorithms used in California are already quite effective at accurately identifying unique types of land cover. This builds confidence in the field, however parameters could be similarly tweaked to produce an even better classification. This study can be useful for improving crop and water planning

Improved Difference Images for Change Detection Classifiers in SAR Imagery Using Deep Learning

Satellite-based Synthetic Aperture Radar (SAR) images can be used as a source of remote sensed imagery regardless of cloud cover and day-night cycle. However, the speckle noise and varying image acquisition conditions pose a challenge for change detection classifiers. This paper proposes a new method of improving SAR image processing to produce higher quality difference images for the classification algorithms. The method is built on a neural network-based mapping transformation function that produces artificial SAR images from a location in the requested acquisition conditions. The inputs for the model are: previous SAR images from the location, imaging angle information from the SAR images, digital elevation model, and weather conditions. The method was tested with data from a location in North-East Finland by using Sentinel-1 SAR images from European Space Agency, weather data from Finnish Meteorological Institute, and a digital elevation model from National Land Survey of Finland. In order to verify the method, changes to the SAR images were simulated, and the performance of the proposed method was measured using experimentation where it gave substantial improvements to performance when compared to a more conventional method of creating difference images

Recent Advancement of Synthetic Aperture Radar (SAR) Systems and Their Applications to Crop Growth Monitoring

Synthetic aperture radars (SARs) propagate and measure the scattering of energy at microwave frequencies. These wavelengths are sensitive to the dielectric properties and structural characteristics of targets, and less affected by weather conditions than sensors that operate in optical wavelengths. Given these advantages, SARs are appealing for use in operational crop growth monitoring. Engineering advancements in SAR technologies, new processing algorithms, and the availability of open-access SAR data, have led to the recent acceleration in the uptake of this technology to map and monitor Earth systems. The exploitation of SAR is now demonstrated in a wide range of operational land applications, including the mapping and monitoring of agricultural ecosystems. This chapter provides an overview of—(1) recent advancements in SAR systems; (2) a summary of SAR information sources, followed by the applications in crop monitoring including crop classification, crop parameter estimation, and change detection; and (3) summary and perspectives for future application development