Hyperspectral images (HSI) provide a rich source of data for remote sensing applications, offering extensive spectral data about the Earth's surface. Object detection in HSI remains a challenging process with various application areas in environmental monitoring, agriculture, and geospatial analysis. The development of deep learning (DL) models for HSI object detection paves the way for new opportunities in advanced remote sensing analysis. DL models enable the automated and reliable detection of target objects. Particularly, convolutional neural networks (CNNs) can handle the high-dimensional nature of hyperspectral data and efficiently learn complex relationships among spectral patterns and object classes. This results in improved detection performance and reduces the need for manual feature engineering. Therefore, this study presents a Hyperspectral Object Detection using Bioinspired Jellyfish Search Optimizer with Deep Learning (HSOD-JSODL) technique for Enhanced Remote Sensing Analysis. The aim of the HSOD-JSODL method lies in the effectual recognition of interested objects in the HSI using the DL model. To achieve this, the HSOD-JSODL technique employs EfficientDet object detector to recognize various kinds of objects in the HSI. EfficientDet is a recently developed object detector which integrates efficiency via a compound scaling approach and efficient network design. For the classification of detected objects, the HSOD-JSODL technique uses a deep belief network (DBN) classifier model. To improve the object classification results of the DBN model, the JSO algorithm is applied as a hyperparameter optimizer. The simulation analysis of the HSOD-JSODL technique is examined on the HSI dataset, and the outcomes are examined under various measures. The simulation values portrayed the betterment of the HSOD-JSODL technique over compared methods.
INDEX TERMSRemote sensing; Hyperspectral imaging; Deep learning; Object detector; Jellyfish search optimizer I. INTRODUCTION Object detection (OD) is a process of locating objects at distinct scales in imaged scenes and recognizing their category data. It is one of the essential yet difficult processes in modern computer vision (CV) study, with applications, namely medical diagnosis, autonomous driving, industrial machine vision, and satellite remote sensing [1]. Currently, the rapid growth of deep neural network (DNN) methods and largescale, well-annotated data have resulted in breakthroughs in image target recognition. Presently, many works on OD are dependent on imaging with visible light utilizing RGB images) [2]. However, in numerous scenarios, objects cannot This article has been accepted for publication in IEEE Access.
