Presently, deep learning and convolutional neural networks (CNNs) are widely used in the fields of image processing, image classification, object identification and many more. In this work, we implemented convolutional neural network based modified U-Net model and VGG-UNet model to automatically identify objects from satellite imagery captured using high resolution Indian remote sensing satellites and then to pixel wise classify satellite data into various classes. In this paper, Cartosat 2S (~1m spatial resolution) datasets were used and deep learning models were implemented to detect building shapes and ships from the test datasets with an accuracy of more than 95%. In another experiment, microwave data (varied resolution) from RISAT-1 was taken as an input and ships and trees were detected with an accuracy of >96% from these datasets. For the classification of images into multiple-classes, deep learning model was trained on multispectral Cartosat images. Model generated results were then tested using ground truth. Multi-label classification results were obtained with an accuracy (IoU) of better than 95%. Total six different problems were attempted using deep learning models and IoU accuracies in the range of 85% to 98% were achieved depending on the degree of complexity.

翻译：目前，深度学习与卷积神经网络（CNNs）已广泛应用于图像处理、图像分类、目标识别等诸多领域。本研究采用基于卷积神经网络的改进U-Net模型与VGG-UNet模型，对高分辨率印度遥感卫星影像进行自动目标识别，并对卫星数据实施逐像素多类别分类。本文利用Cartosat 2S（空间分辨率约1米）数据集，通过深度学习模型从测试数据中检测建筑物轮廓与船舶目标，准确率超过95%。在另一项实验中，以RISAT-1微波数据（多分辨率）作为输入，从中检测船舶与树木的准确率达96%以上。针对多类别图像分类任务，基于多光谱Cartosat影像训练深度学习模型，并利用地面真值数据验证模型输出结果。多标签分类的交并比（IoU）精度优于95%。本研究通过深度学习模型成功处理六类不同问题，根据任务复杂度差异，交并比精度达到85%至98%的区间范围。